Abstracts & Materials

The integration of Artificial Intelligence (AI) in the chemical industry is transforming the sector toward sustainable development, circular economy practices, and economic growth in India. AI-driven technologies such as predictive analytics, smart manufacturing, process automation, and waste optimization are improving production efficiency, reducing energy consumption, and minimizing environmental impact. In the framework of a circular economy, AI supports resource recovery, recycling, waste management, and the reuse of industrial by-products, thereby promoting efficient utilization of raw materials and reducing industrial pollution. These advancements directly contribute to the goals of sustainable development by encouraging green chemistry, lowering carbon emissions, conserving natural resources, and supporting environmentally responsible industrialization. The Government of India through the Union Budget 2026–27 is expected to strengthen the adoption of AI and digital technologies in chemical manufacturing by increasing investments in innovation, research and development, renewable energy, and industrial infrastructure. Budget initiatives focusing on “Make in India,” green hydrogen, clean energy transition, and smart industrial ecosystems can accelerate the modernization of India’s chemical sector while generating employment and enhancing global competitiveness. Furthermore, the combination of AI, circular economy principles, and sustainability strategies can help Indian chemical industries achieve long-term economic resilience and environmental balance. Thus, AI acts as a bridge connecting technological advancement, sustainable industrial practices, and national economic policies, paving the way for a greener and more efficient future for the Indian chemical industry.

Sustainable agriculture is challenged by the need to balance productivity with efficient resource use and environmental conservation. This research presents GreenScore AI, an artificial-intelligence-driven framework designed to assess and enhance sustainability in smart farming through circular economy principles. The proposed system evaluates key agricultural parameters such as water consumption, energy usage, waste generation, and crop productivity to generate a comprehensive sustainability score. GreenScore AI not only quantifies farm performance but also provides intelligent, data-driven recommendations to optimize resource utilization and minimize waste. By promoting practices such as water reuse, efficient energy management, and recycling of agricultural residues, the system supports the transition from linear to circular farming models. The integration of real-time data and machine learning techniques enables adaptive decision-making tailored to specific farm conditions, and a user-friendly interface ensures that farmers can easily interpret the results and implement suggested improvements. Designed with scalability and affordability in mind, GreenScore AI is particularly relevant for small and marginal farmers; the proposed approach not only bridges the gap between advanced digital technologies and field-level practices but also contributes to building climate-resilient and sustainable agricultural systems.

The rapid expansion of artificial intelligence (AI) workloads has intensified reliance on centralized data centers, resulting in significant energy consumption and infrastructure constraints. Concurrently, billions of edge devices, including smartphones, laptops, and IoT systems, operate at low average utilization, representing a vast pool of underutilized computational capacity. This study presents Netara, a decentralized AI inference framework that applies circular economy principles by transforming idle edge devices into a distributed computing network without requiring additional physical infrastructure. The proposed system consists of a lightweight device agent that activates during idle periods, a task orchestration engine that decomposes workloads into micro-tasks, a consensus-based verification mechanism that ensures reliability across heterogeneous nodes, and a REST-based interface for seamless integration. By extending the lifecycle utilization of existing hardware, the framework reduces dependence on energy-intensive data centers and minimizes the need for new infrastructure. Preliminary evaluation of a controlled prototype network demonstrates the feasibility of parallel task execution and fault-tolerant inference, with projected cost reductions of 60–80% relative to comparable centralized inference systems, alongside corresponding decreases in energy demand. The proposed approach contributes to sustainable AI infrastructure by establishing decentralized, edge-based inference as a scalable model aligned with circular economy principles, particularly for improving access to affordable AI in resource-constrained regions and the Global South.

Municipal solid waste management in India is a complex socio-technical challenge shaped by rapid urbanization, changing consumption patterns, heterogeneous waste composition, infrastructure deficits, informal recycling networks, environmental externalities, and evolving policy mandates. This paper presents an integrated decision-support methodology for evaluating and optimizing urban waste-management systems in the Indian context. The proposed framework combines five complementary analytical layers: life-cycle assessment for environmental quantification, system dynamics for long-term, feedback-rich simulation, artificial-intelligence-based forecasting for waste-generation prediction, fuzzy TOPSIS for technology ranking under uncertainty, and reinforcement learning for adaptive policy sequencing. The framework is designed around Indian municipal realities, including high organic fractions, variable collection efficiency, monsoon effects, festival-linked waste spikes, legacy dumpsites, dependence on the informal sector, and limited municipal finance. Recent policy and empirical research are incorporated to continue the expansion of the Swachh Bharat Mission–Urban 2.0, to address India’s approximately 150,000 tonnes per day of urban waste generation, and to address global projections that municipal solid waste may increase substantially by 2050. The methodology supports comparative assessment of sanitary landfilling, composting, anaerobic digestion, material recovery, waste-to-energy, refuse-derived fuel systems, and biomining of legacy waste. By coupling environmental, economic, technical, social, and policy variables, the framework provides a reproducible, transparent, and adaptive basis for sustainable waste-management planning in Indian cities.

The unsustainable nature of linear water management models, which create freshwater waste and consume excess treatment energy and lack hydrosphere visibility during climate change and urbanization periods, has become evident through global water scarcity and rising hydrological disasters. The project introduces a Digital Twin–Circular Nexus framework which combines Digital Twin (DT) technology and Circular Water Technologies (CWT) to create a comprehensive water management system. Digital Twin systems establish exact virtual models of infrastructure systems, covering treatment plants and river basins, by utilizing Internet of Things sensors, artificial intelligence analytical tools, and satellite information to monitor systems in real time and make predictive models. The Circular Water Technologies process, which includes Membrane Bioreactors and Granular Sludge systems, enables wastewater to undergo advanced treatment for recycling purposes while closing material loops to recover valuable resources and reduce freshwater consumption. The system operates through three architectural levels: micro systems for plant optimization, meso systems for urban leak reduction and demand management, and macro systems for basin-scale forecasting and transboundary equity. This system delivers transformative operational efficiencies, substantially reducing freshwater usage and significantly abating maintenance costs, while enhanced resilience against floods and droughts is enabled by advanced probabilistic modeling. The development of Digital Twin Earth Hydrology depends on cloud-based artificial intelligence systems which provide solutions for data interoperability and cybersecurity obstacles. The data-driven concept directs water systems toward circular operations while achieving efficient outcomes which can be implemented worldwide to create a sustainable future for hydrosphere management.

The uncontrolled proliferation of Parthenium hysterophorus, an aggressive invasive weed widespread across India, poses severe ecological, agricultural, and public health challenges. Concurrently, the discharge of heavy metal ions like chromium (Cr), lead (Pb), and cadmium (Cd), alongside pharmaceutical pollutants from industrial effluents, has emerged as a critical environmental concern, threatening aquatic ecosystems and human health. This study proposes the valorization of Parthenium hysterophorus biomass into engineered biochar as a low-cost, sustainable adsorbent for the simultaneous removal of heavy metals and pharmaceutical contaminants from industrial wastewater, addressing both invasive weed management and water pollution within a circular economy framework. The proposed methodology involves pyrolytic synthesis of biochar from Parthenium hysterophorus under optimized temperature and residence time conditions, followed by chemical activation to enhance surface area, porosity, and functional group density. Characterization of the synthesized biochar will be conducted using BET surface area analysis, FTIR spectroscopy, SEM-EDX, XRD, and TGA to establish structure–property correlations. Batch adsorption experiments will investigate the effect of pH, contact time, adsorbent dosage, and initial pollutant concentration on removal efficiency in both single and multi-pollutant systems. Adsorption kinetics, equilibrium isotherm modeling, and thermodynamic studies will be performed to elucidate the underlying adsorption mechanisms. This study aims to demonstrate that Parthenium-derived biochar serves as a high-performance adsorbent in complex multi-pollutant systems, offering a scalable and economically viable solution for industrial wastewater treatment. The dual-benefit approach of invasive weed valorization and pollutant remediation positions this work as a meaningful contribution toward sustainable water management.

Oil-contaminated water streams continue to pose significant environmental and resource recovery challenges, necessitating the development of sustainable, cost-effective treatment technologies. Commercial cleaning sponges are inexpensive, widely available, and possess highly interconnected porous networks, making them attractive candidates for absorbent-based water remediation. However, their intrinsic amphiphilicity and large pore sizes often permit the rapid penetration of both water and oil, limiting separation selectivity. In this work, a simple, material-efficient surface modification strategy based on the deposition of natural lotus wax is proposed to regulate capillary-driven liquid transport in commercially available sponges. Pristine and wax-modified sponges will be evaluated in terms of pore morphology, pore size distribution, wettability, absorption performance, and liquid infiltration behavior. Static and dynamic contact angle measurements and time-dependent uptake experiments will be employed to investigate wetting transitions and transport kinetics. Pore characteristics will be quantified through image analysis, while the infiltration behavior of water and oil will be interpreted using capillary pressure and Washburn infiltration concepts to establish relationships between pore architecture, surface energy, and liquid transport dynamics. The study aims to elucidate how natural wax deposition alters pore geometry and surface wettability, thereby affecting liquid invasion in highly interconnected porous networks. It is anticipated that even low wax loadings will significantly retard water penetration while preserving rapid oil uptake, thereby improving oil–water selectivity without extensive modification of the underlying sponge structure. By combining an inexpensive commercial substrate with a bio-derived surface treatment, the proposed approach is expected to provide a scalable and environmentally benign route for selective oil removal from contaminated water streams, further contributing to a mechanistic understanding of capillary transport in porous absorbents and supporting the development of materials for circular water management and resource recovery applications.

Electrocoagulation (EC) has emerged as an effective technique for sewage sludge dewatering; however, its practical application is often limited by high energy consumption and operational costs. In this study, EC with iron electrodes was optimized for sludge dewatering by systematically varying key operational parameters, including applied voltage, electrode spacing, and reaction time. The dewatering performance was evaluated using specific resistance to filtration (SRF) and moisture content reduction. To enhance process efficiency and promote resource recovery, the EC-treated sludge was further converted into iron-loaded biochar (Fe–BC) via thermochemical treatment. The synthesized Fe–BC was reutilized as an additive in subsequent EC cycles to improve floc formation, electrical conductivity, and overall dewatering efficiency. The impact of Fe–BC addition on energy consumption, treatment time, and dewatering performance was systematically investigated. Furthermore, the reusability of Fe–BC was assessed over multiple cycles to determine its performance stability and functional lifespan. Changes in physicochemical properties of Fe–BC after repeated use were analyzed to understand the mechanisms governing its effectiveness. The results demonstrate that Fe–BC significantly enhances EC performance while reducing energy requirements, with a gradual decline observed beyond a certain number of reuse cycles.

The interaction between pore structure, permeability, pressure gradients, and transport mechanisms such as advection, diffusion, and mechanical dispersion governs fluid flow in porous media. In reactive transport systems, dissolved chemical species undergo geochemical phenomena such as mineral dissolution, precipitation, ion exchange, and aqueous speciation while moving across interconnected pore spaces. Understanding subsurface processes is made easier by Reactive Transport Modelling (RTM) in porous media. In practice, RTM uses one of the Operator Splitting (OS) approaches, known as the Sequential Non-Iterative approach, which solves the equations of transport and reactions separately rather than simultaneously. This study presents a coupled framework integrating COMSOL Multiphysics, MATLAB, and PHREEQC to solve RTM problems. The coupling approach uses COMSOL’s transport-solving capabilities with PHREEQC’s geochemical equilibrium and kinetic calculations, with MATLAB serving as a data exchanger and OS interface. The benchmark problem used is a 2D axisymmetric problem: pesticide transport and reaction in soil. The effective saturation plotted along with the velocity field helps us understand the rate and direction at which the pesticides move through the soil. After pesticide infiltration, the concentration of aldicarb reaches a steady state as the plume gets concentrated around the source, while aldicarb sulfoxide affects a larger soil volume, exhibiting a distinct ring-band pattern after 10 days. The benchmark study reveals that the coupled framework can accurately capture the evolution of contaminated plumes in soil. The developed methodology can be further extended towards applications in geological CO₂ sequestration; understanding these coupled processes is critical for predicting long-term storage stability, mineral trapping mechanisms, porosity evolution, and environmental consequences of carbon capture and storage (CCS) technologies.

A critical component of water quality management is ensuring that concentrations of heavy metal ions remain below their permissible thresholds. Although specific heavy metals (HMs) at low concentrations are beneficial as essential minerals for human health and micronutrients for plants, their excessive presence poses risks of cumulative toxicity across the biosphere. Therefore, the development of dependable and sensitive tools for quantitative heavy metal detection over a broad concentration range is important. To address this need, a simple and scalable sensor platform based on polymer test strips was developed. We report the development of a sensor for Cu, Pb, Ni, Hg, and Zn in aqueous environments, capable of operating across concentrations from 1 nM to 10 mM while maintaining an error margin of 10% or less. Test strips were fabricated by drop-casting poly(3,4-ethylenedioxythiophene)-poly(styrene sulfonate) (PEDOT:PSS) onto glass substrates, followed by dip-coating in aqueous heavy metal ion solutions. The resulting polymer–metal systems were analyzed using Fourier transform infrared (FTIR) spectroscopy, and calibration curves were constructed from spectral region areas. Interactions between polymer groups and metal ions are interpreted through Pearson’s Hard and Soft Acids and Bases (HSAB) theory, revealing selective binding mechanisms. This approach uses FTIR spectral shifts to indicate structural and electronic changes within the polymer matrix. The method is scalable, cost-effective, and compatible with handheld FTIR devices, offering a practical alternative to ICP-OES for portable and on-site monitoring of heavy metals, yielding substantial advantages from both societal and commercial viewpoints.

Polycyclic aromatic hydrocarbons (PAHs) are persistent and highly toxic organic pollutants frequently detected in coal mine wastewater, posing severe risks to aquatic ecosystems and human health due to their carcinogenicity, bioaccumulation, and environmental persistence. Conventional remediation strategies, including adsorption, biological treatment, and advanced oxidation processes, are often constrained by high operational costs, slow degradation kinetics, and limited removal efficiencies. In this work, we propose the development of bio-derived photocatalytic micromotors based on renewable pectin for the active degradation of PAHs under visible-light irradiation. Pectin, a naturally abundant and biodegradable polysaccharide, was employed as the structural matrix for synthesizing microparticles via a hydrothermal approach, followed by asymmetric deposition of titanium dioxide (TiO₂) to impart photocatalytic functionality. The synthesized particles were characterized to evaluate their morphology, surface chemistry, and structural integrity, confirming the successful fabrication of TiO₂-functionalized pectin microparticles suitable for photocatalytic applications. Ongoing studies are focused on investigating the degradation of representative PAHs under blue-light irradiation in the presence of hydrogen peroxide, where TiO₂ is expected to generate reactive oxygen species capable of oxidizing PAHs into less harmful products. The active photocatalytic platform enhanced catalyst–pollutant interactions, leading to efficient degradation of representative PAHs. At an initial concentration of 10 ppm, TiO₂-functionalized pectin micromotors achieved ≈100% degradation of anthracene and ≈70% degradation of naphthalene after 24 h of blue-light irradiation. These findings demonstrate the effectiveness of bio-based photocatalytic micromotors for degrading persistent organic pollutants and highlight their potential as sustainable and environmentally friendly materials for coal mine wastewater remediation.

There is growing global interest in advancing wastewater treatment methods to effectively remove polycyclic aromatic hydrocarbons, which are persistent, toxic, and carcinogenic contaminants. Conventional techniques are ineffective at removing these compounds. Photocatalysis is one of the best oxidation techniques utilising solar energy, degrading these persistent compounds into non-harmful products; however, heterogeneous photocatalytic degradation of water pollutants presents a considerable challenge due to its complex nature. In this work, we synthesized ZnFe₂O₄/TiO₂/rGO nanocomposites for the photocatalytic degradation of naphthalene under visible light irradiation. XRD analysis confirmed the formation of cubic-phase zinc ferrite and tetragonal-phase titanium dioxide in the composite. Morphological analysis revealed the successful integration of the ZnFe₂O₄/TiO₂ nanocomposite with rGO nanosheets, resulting in a well-defined heterojunction. This heterojunction facilitates efficient exciton separation and transport, suppresses charge carrier recombination, and enhances the generation of reactive oxygen species, thereby playing a crucial role in the photocatalytic degradation of naphthalene. The ZnFe₂O₄/TiO₂/rGO nanocomposites showed improved photocatalytic performance compared to bare ZnFe₂O₄ and TiO₂ nanoparticles. The increased efficiency resulted from their narrow bandgap, improved visible-light absorption, and enhanced charge-carrier separation and transport.

Multifunctional materials can be used to develop highly responsive catalysts for degradation of harmful chemicals by leveraging multiple energy sources simultaneously; these can surpass traditional environmental clean-up techniques, typically based on photocatalysis, and minimize inefficiencies while addressing the challenge of intensive energy consumption. In this study, we demonstrate the efficacy of this multi-stimuli approach by using bismuth ferrite (BFO), a multiferroic material capable of being activated by a combination of light (photocatalysis) and ultrasound (piezo-catalysis), to facilitate the breakdown of organic dyes. This unique combination of multiferroic BFO with multi-stimuli inputs demonstrates an innovative approach to pollutant degradation compared to conventional approaches that rely on photocatalysis alone. Our work focuses on examining the multi-stimuli catalytic efficiency of BFO nanoparticles for the degradation of Rhodamine B (RhB) at a concentration of 20 mg L⁻¹, significantly exceeding the concentrations (5–10 mg L⁻¹) documented in prior photo- and piezo-catalysis investigations. The simultaneous utilization of these energy sources results in markedly faster reaction rates compared to using a single energy source, paving the way for the development of adaptable and versatile technologies based on multiferroic materials, designed to perform efficiently under diverse and evolving conditions, for effectively cleaning real-world wastewater and environmental remediation.

Direct contact membrane distillation (DCMD) is a thermally driven desalination technique that has gained increasing attention for high-salinity water treatment due to its ability to operate at low temperatures while delivering high separation efficiency and effectively utilizing low-grade heat sources. In this study, a PVDF hollow fiber membrane engineered with a sandwich tri-layer porous morphology was employed to enhance vapor transport and reduce mass-transfer resistance. The membrane exhibited a hydrophobic surface with a water contact angle of 115°, ensuring stable operation without pore wetting. DCMD performance was evaluated using a 3.5 wt% NaCl feed solution, achieving an average permeation flux of 7.5 L/m²h and a salt rejection exceeding 99.7% with a hot saline water stream at 80°C and a cold distillate stream at 18°C. The system demonstrated stable long-term operation over a continuous 10-hour run, confirming the suitability of the PVDF hollow fiber structure for energy-efficient and reliable desalination applications.

One of the most recent advancements in the enhancement of pollutant removal in municipal wastewater treatment is Chemically Enhanced Primary Treatment (CEPT). By treating the primary stage of wastewater treatment with coagulants and flocculants, there is an increase in the removal of suspended solids and particulate organic matter, along with an overall improvement in treatment efficiency and organic load reduction provided to subsequent downstream biological treatment processes. CEPT does, however, increase the rate of chemical consumption and sludge generation, as well as operational costs and the hazards of chemical handling and disposal. At the same time, hybrid decentralized wastewater treatment systems have been gaining popularity, presenting an even more viable option as they focus on treating domestic wastewater in the absence of centralized treatment systems; these systems combine one or more treatment processes such as septic tanks, Up-flow Anaerobic Sludge Blanket (UASB) reactors, and constructed wetlands to promote organic matter removal without high energy or operational costs, and have been proven through numerous studies to remove high and low levels of Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD), and Total Suspended Solids (TSS) from domestic wastewater. In this regard, improving the hydrolysis and solubilization of complex organic matter during the initial stages of treatment has been identified as crucial for augmenting subsequent biological processes. The present study examines current treatment methods and hybrid configurations to gain insights into their mechanisms, treatment efficacy, benefits, and shortcomings, and elucidates major technological deficiencies while indicating possible avenues for the creation of novel, sustainable methods to enhance the primary stages of wastewater treatment while minimizing chemical use and lowering operational costs.

Gas hydrates, or clathrate hydrates, are crystalline compounds in which water molecules form hydrogen-bonded cages that encapsulate guest gases such as CO₂ and CH₄ under high-pressure, low-temperature conditions. Naturally occurring in deep-sea sediments and permafrost, these hydrates also hold promise for technological applications, including gas storage, separation, transportation, greenhouse gas sequestration, wastewater treatment, desalination, and flow assurance in oil and gas pipelines. However, their inherently slow formation kinetics remain a major limitation for large-scale carbon capture and storage. In this work, molecular dynamics (MD) simulations are employed to examine the impact of glucoside-based additives – caprylyl glucoside, decyl glucoside, lauryl glucoside, and their sulphonated derivatives – on CO₂ hydrate nucleation and growth. The influence of pressure, temperature, and additive concentration is systematically investigated to provide molecular-level insights into glucosides as kinetic hydrate promoters. Our results demonstrate that glucoside-based promoters significantly enhance hydrate formation and growth, advancing the understanding of hydrate–promoter interactions and underscoring their potential for efficient, sustainable CO₂ capture and storage strategies.

Transitioning rural energy systems away from fossil-dependent technologies toward cleaner alternatives is central to circular energy strategies in developing economies. However, the pathway through which centralised grid expansion interacts with decentralised energy systems, and whether it reinforces or undermines carbon valorisation at the household level, remains empirically underexplored. This paper examines how large-scale rural electrification reshapes the household energy portfolio through technology displacement, fossil fuel dependence, and clean energy capital accumulation, using nationally representative panel data from the CEEW ACCESS survey covering rural households across six Indian states in 2015 and 2018. Exploiting district-level variation in grid connection growth under India’s Saubhagya and DDUGJY programmes, we employ a difference-in-differences design validated across fixed effects, standard first-difference, and augmented first-difference estimators. The results identify three mechanisms through which grid expansion contributes to and partially constrains circular energy system transitions. First, grid expansion displaces fossil-intensive decentralised nodes: diesel-powered microgrids, which constituted 91 per cent of the baseline decentralised supply sample, decline significantly in treated districts, closing a linear fossil extraction-combustion loop at the community level. Second, kerosene lighting declines by 30 percentage points in treated households, representing a substantial reduction in carbon-intensive household energy flows and a shift toward lower-emission grid electricity. Third, and critically, solar home systems are statistically unaffected by grid expansion, indicating that clean decentralised technologies retain their role in the energy portfolio even as fossil-dependent alternatives exit, a finding consistent with circular energy system logic where complementary rather than substitutable technologies coexist. Downstream, treated households accumulate grid-complementary energy capital, including inverters and electrical appliances, deepening system integration and extending the productive utilisation of energy infrastructure. The aggregate transition from diesel and kerosene toward grid electricity and persistent solar represents a partial carbon valorisation pathway: reducing the carbon intensity of energy services delivered while preserving decentralised renewable capacity. However, substituting diesel microgrids for a coal-heavy grid implies that the net emissions benefit depends critically on the carbon intensity of grid supply, underscoring the importance of concurrent grid decarbonisation as a condition for realising the full circular energy potential of rural electrification programs.

CO₂ is one of the major greenhouse gases causing global warming, and carbon capture, utilization, and storage (CCUS) is one of the novel technologies addressing the issue of CO₂ emissions. Among various CO₂ capture methods, amine-based CO₂ absorption remains a widely used technology in industry, with efficiency depending on factors such as operating conditions, the type of amine used, and the effectiveness of gas–liquid contact. Structured packings offer excellent mass transfer efficiency, low pressure drop, good wettability, and high capacity compared to random packings for effective gas–liquid contact, but are also more sensitive to maldistribution, which reduces gas–liquid mass transfer, making it important to improve mass transfer in structured packing. This work focuses on developing a multiphase Eulerian CFD model to predict the hydrodynamics, mass transfer, and CO₂ absorption in monoethanolamine (MEA) for an in-house structured packed column. The computational domain consists of a packed column of 28 cm height and 5 cm diameter, equipped with structured packing of 4.979 cm diameter to enhance gas–liquid contact; the liquid phase consists of aqueous MEA solution filled to a height of 28 cm, with a binary gas mixture of 28.2 wt% CO₂ and 71.8 wt% N₂ introduced from the bottom at 0.021 m/s. The governing momentum, energy, and species transport equations are solved using OpenFOAM v8.0, beginning with an investigation of hydrodynamics followed by detailed modelling of CO₂ absorption in MEA, where CO₂ reacts with MEA to form carbamate (CO₂ + H₂O + 2 MEA ⇌ MEACOO⁻ + MEAH⁺). Mass transfer between gas and liquid phases is incorporated through a Sherwood number correlation, Sh = f(Re, Sc), and the predicted CO₂ loading and capture efficiency at different gas velocities are validated against in-house experimental data; the effective interfacial area across the column is estimated from the gradient of the liquid volume fraction. Unlike the porous media approach commonly used in the literature, which fails to capture the realistic flow distribution across packings that strongly influences mass transfer, the present work employs in-house realistic structured packing geometry, allowing the flow behaviour within the packing elements to be observed, and the resulting CFD predictions of product formation are compared with those obtained using the porous media approach.

Pore-scale modeling has limitations for large-scale transport studies because it is restricted to small domains and may not fully capture reservoir heterogeneity. In this study, porosity data were extracted from a real cylindrical core sample (24 cm length and 5 cm ID) using MATLAB-based image-processing techniques; the workflow integrated MATLAB for data processing and geometry generation, while COMSOL Multiphysics was used for transport simulations. Rock permeability was estimated using the Pape et al. (1998) correlation based on pore-structure characteristics, and the resulting datasets were mapped onto a cylindrical geometry for numerical simulations. The influence of diffusion coefficient, dispersivity, and permeability distributions on tracer transport was analyzed, with both spatial and temporal tracer distributions quantified using residence time distribution (RTD) and dilution index analyses; the dilution index was determined using image-processing-based methods described by Kitanidis et al. (1994) to evaluate mixing and spreading behavior. Variations in permeability and porosity were found to influence flow velocity and RTD, while fractures created preferential flow pathways that accelerated transport and reduced mixing. Further, experiments were conducted in homogeneous and heterogeneous synthetic media to generate controlled data; the movement of the tracer plume at the outlet was analyzed to measure RTD at various flow rates, and images were captured with a high-speed camera to evaluate the dilution index. At lower flow rates, the RTD curves showed a broader distribution with a long tail, while as flow rate increased, tracer breakthrough occurred earlier and the extent of tailing decreased. The qualitative visualization results were consistent with the RTD analysis, thereby linking pore-scale heterogeneity to overall transport behavior. The developed model will be further applied to understand CO₂ transport and flow behavior in sub-surface flow.

Adsorption-based carbon capture has emerged as a promising alternative to conventional amine absorption, offering several advantages. In conventional temperature-swing adsorption (TSA) processes, regeneration is typically performed at a constant heating temperature, which often results in excessive thermal driving forces, irreversible heat transfer, and poor energy utilization; the large regeneration energy requirement during the desorption step remains a major challenge limiting the large-scale deployment of adsorption systems. The present work proposes a simplified thermodynamic framework for analyzing regeneration energy requirements in adsorption-based CO₂ capture systems, introducing the concept of pinch-based thermal management to define optimized desorption conditions during the regeneration of solid adsorbents. The model is employed to analyze different heating strategies, with the baseline constant-temperature case first validated against reported literature results; the validated model is then employed to evaluate various heating strategies and their influence on regeneration energy demand, desorption duration, and thermodynamic efficiency. The model framework is further extended to compare the regeneration behavior of various solid adsorbents such as metal-organic frameworks (MOFs), covalent organic frameworks (COFs), zeolitic imidazolate frameworks (ZIFs), and amine-functionalized silica adsorbents. The expected outcome of this work is the identification of adsorbent materials and regeneration strategies that offer lower specific energy consumption and improved thermodynamic performance, providing a simple yet effective platform for screening emerging adsorbents from an energy perspective and enabling a more performance-oriented evaluation rather than one based solely on adsorption capacity.

The increasing availability of affordable renewable electricity offers a significant opportunity to decarbonize energy-intensive chemical processes. Among various electricity-to-heat technologies, including microwave and induction heating, Joule heating has emerged as a promising option owing to its high thermal efficiency, rapid thermal response, and scalability. While experimental studies have demonstrated the feasibility of electrically heated catalytic reactors, the development of computational design frameworks for reactor optimization and scale-up remains limited. In this work, a two-dimensional axisymmetric computational fluid dynamics (CFD) model is developed to investigate the coupled electro-thermal-species transport behavior in a Joule-heated steam methane reforming (SMR) reactor, incorporating the heterogeneous reaction kinetics proposed by Xu and Froment. The CFD predictions are validated against the experimental temperature profiles reported by Wismann et al., demonstrating good agreement with the measured data. The model further provides detailed insights into the interplay between heat transfer, mass transfer, and reaction kinetics through analyses of Nusselt and Sherwood numbers along with characteristic time scales. In addition, the influence of axial power distribution on reactor performance is systematically examined while maintaining the same average power input; three power profiles – constant, linearly increasing, and linearly decreasing – are evaluated for their impact on temperature distribution, methane conversion, hydrogen yield, and reactor length reduction. The simulations reveal that a linearly decreasing power profile produces a more uniform temperature distribution, resulting in enhanced methane conversion and hydrogen yield, and that comparable reactor performance can be achieved at significantly reduced reactor lengths, highlighting the potential of power tailoring as a strategy for process intensification in electrically heated catalytic reactors.

Electrochemical utilization of CO₂ has significant potential to convert CO₂ into valuable chemicals such as CO, and recent advances in gas diffusion electrodes (GDEs) have improved CO₂ electrolyzer performance. However, the catalyst layer (CL) is multiscale, multiphase, and multiphysics, making accurate modelling a challenge; various 1D models report transitions from a kinetic to a mass-transfer-controlled regime as applied potential increases, but the physical representation of CO₂ gas–electrolyte liquid distribution and wettability underlying the mass transport limitations remains simplified. In this study, a validated 1D model is combined with pore-scale Volume of Fluid (VOF) simulations to examine how gas–liquid distribution and wettability influence mass transfer limitations in the CL. A 1D model of a microfluidic CO₂ electrolyzer with an Ag GDE is developed and validated against experiments; simulations are performed at 1 atm and 0.5 M KHCO₃ electrolyte, with reduction potential varying from −0.5 V to −2 V (vs. RHE). The 1D model is implemented in COMSOL Multiphysics, and the gas–liquid distribution is investigated using the VOF method in ANSYS Fluent. The developed 1D model is analyzed for two wettability conditions in the CL – wetted (thin electrolyte film) and flooded (electrolyte-filled pores) – and the mass transfer analysis identifies the gas–liquid mass transfer coefficient as the sensitive parameter, which is commonly overestimated in the literature using diffusivity–film-thickness relations. From VOF simulations, under hydrophilic conditions the CL remains flooded irrespective of increasing contact angle at 1 atm, showing that flooding-dominant transport provides a more realistic description of gas–liquid distribution in the CL than thin-film transport assumptions.

India has complex waste management challenges due to its mixed waste streams. Thermochemical conversion pathways are effective for extracting value from this waste as gaseous or liquid fuels, with efficiency and product formation determined by process temperature and feedstock composition. In this ongoing study, the characteristics of a mixed feedstock and their impact on syngas production and quality in a high-temperature gasification system are explored. The feedstock consisted of mixed sawdust, low-density polyethylene (LDPE) garbage bags, and kraft paper bags, shredded and mixed in different ratios. For sawdust mixtures, moisture and volatile matter were 7.2–7.36% and 85–86.9%, respectively, with ash content between 1.4–2.1%; for kraft paper, these parameters were 4.478%, 89.1%, and 6.86%, respectively; and for the garbage bag sample, they were 0.395%, 87.96%, and 9.36%, respectively, with ash content attributable to fillers and additives. The results of the proximate analysis show that both particle size and feedstock composition influence output and reaction kinetics; moisture and volatile matter, along with particle size and mixture ratios, play a role in determining the quality of syngas produced. These findings will aid in identifying ideal feedstock pre-treatment conditions for chemical recycling.

Perovskite solar cells (PSCs) have recently achieved power conversion efficiencies (PCEs) comparable to conventional silicon photovoltaics. Despite this progress, commercialization remains limited by poor long-term stability and the need for fabrication in inert atmospheres, typically in glove boxes. To address the stability concerns, magnesium (Mg), an earth-abundant, non-toxic element, was explored as a partial substitute for lead in MAPbI₃ perovskites; Mg incorporation was designed to block iodine vacancy sites, thereby reducing oxygen diffusion and mitigating degradation pathways. To overcome current challenges, precursor synthesis (MAI: methylammonium iodide and PbI₂: lead iodide), ink preparation, and doping procedures were performed under cost-effective, open-air laboratory conditions (temperature: 30±2°C; relative humidity: 35±4%) within a fume hood, eliminating the need for sophisticated inert-atmosphere processing. Mg-doped perovskites were subjected to different annealing conditions, including conventional annealing and vacuum annealing (VA), to evaluate the impact of thermal treatment. Comparative analysis revealed that Mg doping alone increased the average grain size to ≈350 nm, while the combination of Mg doping with VA produced significantly larger grains (≈850 nm), as confirmed by SEM analysis. Mg-doped perovskite films subjected to vacuum annealing exhibited significantly improved moisture resistance, confirmed through climatic test studies at 35°C and 80–85% relative humidity, conditions known to accelerate material degradation. In addition to improved durability, the films exhibited a twofold enhancement in power conversion efficiency (PCE) under low-humidity conditions. Collectively, these results highlight the synergistic effect of Mg incorporation and vacuum thermal annealing in mitigating moisture-induced degradation pathways while simultaneously boosting device performance, highlighting the potential of compositional engineering with Mg and process optimization via vacuum annealing as complementary strategies to improve both the stability and efficiency of perovskite solar cells, paving the way for environmentally benign and commercially viable perovskite photovoltaics.

The rising demand for cost-effective and sustainable energy storage solutions has necessitated the exploration of alternative battery technologies beyond traditional lithium-ion batteries. Sodium-ion batteries (SIBs) have emerged as a viable alternative for large-scale energy storage systems due to the natural abundance, extensive geographical distribution, and low cost of sodium resources. Among the numerous anode candidates, hard carbon has attracted considerable interest due to its high reversible capacity, suitable working potential, and compatibility with sodium-ion storage mechanisms. In this study, banana peduncle, an abundant agricultural waste biomass, is used as a sustainable precursor for the synthesis of hard-carbon anode materials. The biomass is initially subjected to acid hydrolysis to remove impurities and partially decompose lignocellulosic components, followed by potassium hydroxide (KOH) activation to develop a porous structure and increase the availability of active sites; the activated precursor is subsequently carbonized under an inert atmosphere to obtain hard carbon with a disordered microstructure favorable for sodium-ion storage. Structural and morphological characteristics of the synthesized materials are investigated using X-ray diffraction (XRD), Raman spectroscopy, and scanning electron microscopy (SEM). The combined acid hydrolysis, chemical activation, and thermal carbonization processes are expected to produce hard carbon with enlarged interlayer spacing, abundant defect sites, and hierarchical porosity, facilitating sodium-ion diffusion and storage. This work presents a cost-effective, environmentally sustainable approach to valorize banana peduncle waste into high-value hard carbon materials and highlights its potential as an anode material for next-generation sodium-ion batteries.

The rise in CO₂ emissions underscores the need for efficient carbon capture and low-energy sorbent regeneration. Conventional desorption methods based on a purge gas are either slow or energy-intensive, as they rely on indirect heat transfer mechanisms inherently limited by gas–solid contact efficiency; electrified heating using renewable energy presents a promising pathway to decarbonise energy-intensive processes in the chemical industry. Electrified approaches such as microwave swing adsorption (MSA) enable rapid and selective volumetric heating of the solid phase, significantly reducing desorption time and energy consumption. MSA has demonstrated improved efficiency across fluidised, packed, and structured bed configurations, each with distinct multiphase flow and heat transfer characteristics; however, there is a lack of mechanistic understanding of microwave–material interactions coupled with desorption kinetics. In an in-house reactor system, adsorption capacity, desorption efficiency, and the influence of varied flowrates and desorption temperatures on gas–solid interaction and regeneration performance are analysed for CO₂ capture using Zeolite 13X. Microwave heating desorption cycles were subsequently conducted to assess the viability of microwave irradiation as a selective heating mode in a packed bed environment, and the evolution of temperature profiles within the bed provides insight into the energy deposition characteristics of microwave heating and its implications for bed-scale heat and mass transfer. A key focus of this work is to provide a rigorous basis for isolating the contributions of flow dynamics and heat transfer, since understanding these multiphase interactions is central to translating laboratory-scale performance into scalable, industrially relevant systems.

Post-combustion carbon capture is considered one of the promising approaches for reducing industrial CO₂ emissions and supporting sustainable energy transition strategies. In the present study, a pilot-scale Vacuum Pressure Swing Adsorption (VPSA) system integrated within a mobile containerized unit was experimentally investigated for cyclic CO₂ capture using zeolite 13X beads as the adsorbent material. The zeolite bead size ranged from 1.5–2.5 mm, and the adsorption columns possessed an internal diameter of 0.45 m with a total height of 1.5 m and a packed bed height of 0.70 m. The VPSA system consisted of two adsorption columns operated alternately under adsorption and vacuum desorption conditions to ensure continuous cyclic operation. A simulated flue gas mixture containing air and CO₂ in an 80:20 ratio was supplied from the bottom of the packed bed at a total flow rate of 125 SLPM using a compressor. During adsorption, selective adsorption of CO₂ occurred within the zeolite 13X packed bed, while regeneration was achieved using a vacuum pump under desorption conditions; the adsorption process exhibited exothermic characteristics, with bed temperatures reaching nearly 110°C during cyclic operation. System performance was evaluated over five consecutive adsorption–desorption cycles through breakthrough analysis, adsorption capacity, CO₂ production rate, pressure drop characteristics, and cyclic stability studies. The experimental investigation demonstrated stable VPSA operation with effective CO₂ separation under industrially relevant operating conditions, highlighting the potential of zeolite 13X-based packed bed systems for post-combustion carbon capture applications.

With the growth of the human population, the need for sustainable energy resources has increased substantially; large-scale utilisation of fossil fuels would leave few viable energy resources for future generations, making renewable and clean sources of energy – Green Energy Resources – essential. Biological hydrogen (H₂) production (BHP) enhancement through the use of nanoparticle (NP) supplements in the growth media has recently emerged as a promising approach; nanoparticles, including metal and metal oxide NPs, have shown significant improvement in BHP. Suitable bacteria such as C. butyricum, used as inoculum together with gold nanoparticles (AuNPs), provided an effective approach for H₂ production from sucrose, while Kappaphycus alvarezii and sludge were also processed for bio-hydrogen production. Aqueous phase reforming with 7.5 wt% Au was found to give a 62.3% higher yield from synthetic wastewater containing sucrose as feed under anaerobic culture, compared to a control applying the minimum amount of AuNPs; overall, the catalyst achieved 61.25% bio-hydrogen production. The maximum bio-hydrogen yield was 36.1% for a 2:1 (sludge:algae) ratio at 360°C. The high ratio of acetate to butyrate and low ethanol production observed in the presence of AuNPs is associated with a significant increase in H₂ production. With the aim of expanding applications of bio-nanotechnology for hydrogen production, AuNPs were also biosynthesized via aqueous-phase reforming of a synthetic compound (brewery wastewater) supported on activated carbon; these AuNPs demonstrated strong catalytic performance for the degradation of industrial pollutants, making the environment cleaner and more sustainable. Overall, hydrothermal gasification (aqueous phase reforming) yielded syngas, biochar, and enhanced H₂ production via liquid-phase formation, while anaerobic digestion of cyanobacterial biomass broke down complex organic and inorganic compounds in industrial wastewater, facilitating an efficient reforming process.

The rapid growth of electric vehicles (EVs) has led to a significant increase in lithium-ion batteries, posing both environmental challenges and resource wastage concerns. Many batteries are removed from EV applications when their State of Health falls below 70%, yet they still have sufficient usable capacity for secondary applications such as stationary energy storage. This paper proposes an AI-driven Smart Battery Second-Life Decision System that enables classification of used EV batteries into reuse, repurpose, or recycle pathways. The system senses key battery parameters, including voltage, current, temperature, and internal resistance, using an embedded hardware platform; these inputs are processed through a machine learning model trained to estimate battery health metrics such as State of Health (SoH) and Remaining Useful Life (RUL). Based on predictive analytics, the system performs automated decision-making to determine optimal second-life utilization: batteries with higher health are redirected for reuse in EV or mobility applications, moderately degraded batteries are repurposed for stationary storage such as solar energy systems, and critically degraded batteries are sent for recycling. The proposed solution contributes to circularity in the battery industry by extending battery lifecycle, reducing waste, and improving resource efficiency, while enabling scalable and cost-effective decision intelligence for sustainable battery management in emerging EV ecosystems.

Due to the massive increase in the use of lithium-ion batteries in electronic devices and electric vehicles, there has been a drastic increase in battery demand along with economic and environmental concerns. Conventional approaches, including pyrometallurgy, hydrometallurgy, and direct recycling methods, are widely used for recovering critical metals such as Li, Ni, Co, and Mn from spent or dead Li-ion batteries; however, conventional hydrometallurgical methods use toxic acids and bases, while pyrometallurgy uses very high temperatures. Here, we demonstrate a green polyol-based metallurgical approach using citric acid in ethylene glycol (CAEG), which serves dual functions – leaching and forming a complex precipitate – during the extraction of lithium from LiFePO₄ (LFP) black mass. LFP black mass was pyrolyzed at elevated temperatures to remove volatile impurities, and the pyrolyzed black mass was characterized using techniques such as XRD, FTIR, ICP-MS, and TGA to understand its chemical composition. Leaching was performed using the CAEG solution, and the duration of precipitation was optimized; the leachate and residue were also characterized to evaluate leaching efficiency. It was found that >90% of the Li can be selectively extracted using the CAEG method, demonstrating that a polyol-based metallurgical approach can be a viable green method to extract Li from LFP black mass.

Lithium-ion batteries (LIBs) are central energy-storage technologies for modern electrification, particularly in electric vehicles, and are increasingly being deployed in stationary grid energy-storage applications. Recycling lithium from spent batteries allows valuable resources to be reintegrated into the supply chain, thereby supporting the development of a circular economy and advancing sustainable development. Liquid–liquid extraction is considered an efficient and cost-effective approach for lithium recovery. In this study, molecular dynamics simulations were carried out to examine the selective separation of lithium using deep eutectic solvents and ionic liquids, followed by experimental validation. The work provides an in-depth discussion of the extraction mechanism, solvation behavior, atomic-level interactions, extraction efficiency, and various structural and dynamical properties.

Lithium-ion batteries (LiBs) are integral to modern technology, powering a broad array of devices, including mobile phones, laptops, electric vehicles, and systems for renewable energy. However, their widespread utilisation presents considerable environmental and social challenges, notably due to the reliance on scarce and hazardous materials that necessitate energy-intensive extraction and processing methods. Recycling of LiBs emerges as a viable strategy to address these issues, offering a method to reclaim valuable metals and reduce the demand for the extraction of fresh raw materials. Furthermore, recycling initiatives have the potential to generate economic opportunities and foster innovation within the battery sector. Conventional recycling methods, including pyrometallurgical and hydrometallurgical processes, are energy-intensive and hazardous. To address this, we developed a novel Ternary Deep Eutectic Solvent (TDES), which is green and composed of Hydrogen Bond Donors and Acceptors in a specific molar ratio. This solvent offers several advantages, such as low freezing points, high thermal stability, low volatility, and tunability. Spent mobile phone batteries were dismantled under water to safely recover active cathode material (ACM), which contained Li, Co, Ni, and Mn. The TDES achieved rapid leaching of the ACM at 90 °C within 30 minutes and at a significantly lower operating temperature (90 °C) than traditional methods (700 °C). High extraction efficiencies of ~99% were achieved for Li⁺, Co²⁺, Ni²⁺, and Mn²⁺. Subsequent stage-wise selective precipitation allowed recovery of high-purity metal oxides of Nickel, Manganese and Cobalt and Lithium is selectively precipitated as Lithium carbonate. Kinetic analysis indicated that leaching was controlled by both liquid-film diffusion and product-layer diffusion for Li and Co. The same TDES was applied to LiNi₀.₈₅Co₀.₁Al₀.₀₅O₂ (NCA), yielding extraction efficiencies of ~68% Ni, ~64% Co, ~92% Al, and ~70% Li, aligning with elemental analysis. Further optimisation of metal recovery from NCA systems is under current investigation. Overall, the developed TDES demonstrates strong potential as a sustainable and efficient medium for LIB recycling.

Lithium has already occupied an unshakable position in the field of energy transformation, due to its near-irreplaceability in the production of high-energy-density batteries for electric vehicles. The world is moving towards zero-emission mobility, which requires the electrification and decarbonization of the transport sector. The new energy market has been expanding rapidly, with a dramatic increase in lithium demand worldwide, estimated at 1,607,000 tons in 2050; meanwhile, the gap between demand and supply is becoming increasingly severe, and a sustainable supply of lithium is essential to the healthy development of the lithium battery market. Therefore, it is imperative to develop new technologies and expand various channels for highly efficient lithium recovery. Currently, several common technologies, such as adsorption, ion exchange, solvent extraction, and membranes, have been developed for lithium recovery from salt lake brine. Among these technologies, solvent extraction using tributyl phosphate (TBP) and FeCl₃ as coextractants offers unique advantages, including excellent lithium selectivity in brines with high Mg/Li ratios, stable cycling performance, and simple operation; however, the conventional TBP technique has several limitations, including poor mass transfer, unstable interfaces, extractant loss, and high energy intensity. Therefore, we propose a new technique in which TBP is encapsulated within Pickering emulsions for efficient extraction of Li from salt brine.

In response to the rising demand for energy storage, researchers are prioritizing improving lithium-ion batteries’ specific energy and volumetric energy capacity. Lead (Pb) stands out as a promising anode option due to its high gravimetric capacity of 569 mAh/g and volumetric capacity of 6452 mAh/g, offering significant potential to enhance the performance of these batteries. Upgrading from discarded lead-acid batteries (LABs) to more contemporary lithium-ion batteries will be an alternative approach to meeting growing demand; LABs have a high lead content, resulting in the highest recovery rate during extraction. Pyrometallurgical processes remain the primary method for lead recycling; however, they pose several drawbacks, including harmful environmental impacts, such as the emission of lead dust and sulfur dioxide (SO₂), and high energy consumption. A few recently developed environmentally friendly hydrometallurgical processes operating at near-ambient temperatures have shown great potential for recovering lead from spent lead-acid batteries; direct dissolution of lead through brine leaching offers a promising, clean recovery pathway for recovering lead chloride from lead-acid batteries. In this work, LAB paste material was separated and dissolved in brine solution to produce PbCl₂ through crystallization, a green recycling technique that transforms waste into resources for active anode material in lithium-ion batteries. A novel strategy is explored to use recovered lead halide (PbCl₂) with two-stage lithiation, offering superior electrochemical performance as a replacement for pristine Pb in the negative electrode.

The global scramble to secure tech metals, battery minerals, and rare earths is encountering a significant bottleneck, and current extraction routes – large, environmentally destructive mines or reliance on risky overseas supply chains – are no longer sufficient. This challenge is addressed by the EXTRACT (Efficient Xtraction and Treatment for Recovery of Advanced Critical-materials and Technologies) programme, through the development of integrated hydrometallurgical methodologies for the efficient extraction, purification, and valorization of critical materials from complex feedstocks, primarily end-of-life lithium-ion batteries and spent electronic assemblies. The approach uses smart chemical recycling, green solvents, and cleaner processes to recover high-purity critical materials from commonly overlooked sources such as low-grade ores, old electronics, and industrial waste, substantially reducing the energy and toxic waste normally associated with metal refining. Beyond recycling, the recovered elements are refined back into high-grade materials ready for use in next-generation batteries; lithium, cobalt, nickel, manganese, copper, aluminum, and graphite are the critical materials that underpin rechargeable battery technologies essential to modern energy storage and electronics. The processing line includes optimized leaching, solvent extraction, and electrowinning-based purification steps that selectively recover these materials at battery-grade purity, enabling direct re-integration into the manufacture of recycled battery cells and high-purity metal products, with real-time process monitoring and closed-loop reagent management minimizing chemical consumption and secondary waste generation throughout. EXTRACT provides a scalable, low-impact hydrometallurgical platform to support critical-material supply chains and accelerate the shift towards a circular battery economy.

Solid-state electrolytes offer both safety and enhanced energy density, making them highly desirable for future batteries; however, the challenge with solid-state electrolytes remains in finding scalable, cost-effective processing techniques. In this work, a dry fabrication technique using polytetrafluoroethylene (PTFE) as a binder for the NASICON-type (Na₃Zr₂Si₂PO₁₂–NZSP) electrolyte is investigated, an approach that eliminates the use of toxic and flammable organic solvents in the electrolyte. The ionic conductivity of the flexible NZSP–3% PTFE electrolyte film is found to be 1.12 × 10⁻⁴ S cm⁻¹; the ceramic NZSP electrolyte contributes to the ionic conductivity, while the uniformly distributed PTFE fibrils offer flexibility. Morphological investigation of this electrolyte film reveals that the fibrillated PTFE holds the NZSP powder together through its 3D fibrous structure. Symmetric cell studies demonstrate good interfacial stability against sodium metal, eliminating dendrite formation up to 100 cycles at 0.1 mA cm⁻². In addition, this approach utilizes abundant sodium metal as the anode, highlighting the capability of dry-processed electrolytes as a scalable pathway towards next-generation solid-state sodium-ion batteries.

An earth-abundant and low-cost lignite coal was employed as a carbon precursor for the synthesis of highly pure synthetic graphite through carbonization, followed by high-temperature graphitization treatment at 2800 °C (SG-2800) and 3000 °C (SG-3000). The results reveal that the microstructures of SG-2800 and SG-3000 are strongly dependent on the graphitization temperature. The SG graphitized at 3000 °C has a perfectly ordered layered structure with a high degree of graphitization, as found from the XRD and Raman studies. SG-3000 is explored as an anode material for a lithium-ion battery (LIB) application. The maximum reversible capacity of 305 mAh g⁻¹ at a current rate of 0.1C was obtained along with 94% capacity retention even after 100 cycles. These findings highlight the potential of lignite coal as an inexpensive and scalable precursor for producing high-performance synthetic graphite suitable for advanced LIB applications.

The depletion of fossil fuels and rising greenhouse gas emissions has intensified the exploration of sustainable energy resources. Sugarcane leaf (SCL) biomass is a promising renewable feedstock for biofuel and biochemical production, and while the application of photocatalysis to biorefinery is of great significance for sustainable development, limited success has been achieved to date. This study aimed to develop an effective delignification strategy for SCL biomass and its subsequent photocatalytic conversion into lactic acid (LA). The initial research focused on developing efficient pretreatment strategies for SCL biomass via microwave-assisted sodium carbonate (SCL-MSC) pretreatment for effective deconstruction of the biomass. Optimization using central composite design identified the ideal pretreatment conditions (508 W, 4 min, 7.87 wt% biomass, 0.87 MSC) that resulted in maximum saccharification yield (RSY of 601 mg g⁻¹ and GY of 231 mg g⁻¹). The optimized cellulose-rich SCL biomass was further converted to LA using a g-C₃N₄-O 50% photocatalyst, which increased the LA yield to 345.39 mg g⁻¹ while reducing the formic acid yield to 30.45 mg g⁻¹. Furthermore, techno-economic analysis (TEA) revealed that the estimated net production cost of LA was $3.32 kg⁻¹, with an ROI of 14.43% and a payback period of 6.93 years; sensitivity analysis revealed that raw material costs and LA market price significantly influenced the economic viability. Overall, this work demonstrates a sustainable and economically feasible strategy for producing lactic acid from sugarcane leaf biomass through integrated microwave pretreatment and photocatalysis.

The increasing demand for sustainable construction materials has driven research toward the development of eco-friendly composites derived from renewable resources. This study focuses on the experimental characterization of bio-based roofing composites reinforced with agricultural fibers and bonded using a natural binder matrix comprising lime and starch, with the objective of developing a lightweight, durable, and thermally efficient roofing material as an alternative to conventional systems. The composite is fabricated by integrating treated agro-fibers with the binder matrix and supplementary materials, followed by molding into corrugated sheet forms, compaction, and controlled curing; fibers enhance tensile strength and insulation properties, while lime contributes to durability and water resistance, and starch improves interfacial bonding. Experimental evaluation includes assessment of physico-mechanical and thermal properties such as density, compressive strength, water absorption, and thermal conductivity. Results indicate that the developed composite exhibits adequate structural performance and improved thermal insulation compared to conventional roofing materials, demonstrating the potential of bio-based composites as sustainable roofing solutions that promote resource efficiency, reduced environmental impact, and applicability in low-cost housing and rural infrastructure development.

The fashion industry is one of the most resource-intensive sectors globally, contributing significantly to environmental degradation through excessive water consumption, greenhouse gas emissions, and large-scale textile waste generation. The prevailing linear “take–make–dispose” model has intensified sustainability challenges, necessitating systemic interventions for resource efficiency and waste reduction. In this scenario, the circular economy offers a regenerative framework aimed at extending product life cycles, improving material recovery, and minimizing environmental impacts across production and consumption systems. This poster presents a conceptual and analytical exploration of circular economy integration within the fashion industry, focusing on its role in enabling sustainable production systems. Key circular strategies, including textile recycling, upcycling, eco-design, and the adoption of sustainable and bio-based materials, are examined in relation to their potential to reduce environmental burdens and enhance resource circularity within supply chains. The study further investigates critical barriers limiting large-scale implementation, such as technological constraints, fragmented supply chain structures, economic feasibility, and limited consumer awareness regarding sustainable fashion practices. Additionally, the analysis emphasizes the importance of multi-stakeholder collaboration involving industry actors, policymakers, and consumers in facilitating effective circular transitions. The need for context-specific, scalable, and economically viable strategies is highlighted to ensure long-term sustainability outcomes. This work contributes to the growing discourse on sustainable industrial ecosystems and aligns with global sustainability agendas, particularly Sustainable Development Goals related to responsible consumption and production.

The global wood-plastic composite market, valued at approximately USD 10 billion and growing rapidly, faces persistent challenges due to its reliance on wood flour as a primary filler. This material is inherently flammable, prone to biological decay, and degrades under high heat. Meanwhile, large-scale coal preparation plants generate millions of tonnes of coal fines and reject material annually, which are typically treated as low-value waste with limited use. This study introduces coal-incorporated polymer composites as a circular-economy solution that addresses the performance gaps of conventional wood-plastic composites and valorizes an abundant domestic industrial by-product. Following detailed characterization, we will modify the coal to improve its compatibility with plastics. These tailored fillers will then be blended at varying loadings using standard molding techniques. The resulting composites will be evaluated for structural strength, thermal resilience, and fire resistance. Leveraging coal’s carbon-rich structure, we anticipate improved flame retardancy and heat tolerance, reducing dependence on expensive chemical additives. Additionally, this waste-derived filler offers clear economic benefits through its low cost and local availability. Ultimately, this research aims to convert industrial coal refuse into valuable structural materials, lowering disposal burdens, fostering sustainable manufacturing, and supporting national resource efficiency objectives.

This paper presents a review of reverse supply chains (RSCs) for used electric vehicle batteries. It integrates existing knowledge across battery recycling, battery reuse, second-life applications, material recovery, and policy frameworks. The review highlights multi-dimensional uncertainties in return quality, quantity, costs, and regulation that determine an efficient RSC or used electric vehicle batteries. While recycling shows strong technical feasibility, its industrial scalability remains limited, and the logistics costs dominate the economics. Second-life deployment at 70–80% residual capacity, although it is established, in reality is constrained by liability, standardised state-of-health assessment, and pricing. Policy analysis shows insufficiently regulated global regulations. The paper concludes by identifying research gaps in integrating scalable supply chain logistics, adopting regulation, and viable second-life business models.

The increasing demand for sustainable and decentralized wastewater treatment has driven advances in biomass-based adsorbents to enhance water quality. This research explores the use of biochar derived from Clitoria ternatea biomass (CT-BC) as an environmentally friendly adsorbent for treating domestic wastewater. Biochar was produced via pyrolysis and tested under various operating conditions to improve pollutant removal efficiency. The adsorption process was analysed using equilibrium and kinetic models to better understand the mechanisms involved in treatment. Advanced characterization methods, including XRD, FTIR, and SEM–EDS, were used to analyse the physicochemical properties of CT-BC. The findings showed a porous, heterogeneous surface with oxygen-rich functional groups, which are ideal for contaminant adsorption. Isotherm and kinetic tests demonstrated that the adsorption was driven by heterogeneous surface interactions and chemisorption. Statistical results confirmed that CT-BC effectively and consistently enhanced wastewater quality at various sampling sites. The results show that biochar made from Clitoria ternatea is an effective, affordable, and eco-friendly option for treating domestic wastewater. Turning biomass waste into a valuable water-purification tool promotes circular resource use and supports sustainable water management. This research supports circular economy principles and helps achieve the UN Sustainable Development Goals on clean water, sanitation, and responsible resource use.

Agricultural residues are often treated as waste, despite their potential for productive reuse in rural infrastructure. This study explores a circular application of pearl millet and paddy straw as roof-insulation materials for naturally ventilated dairy shelters in India, with the aim of reducing heat stress while creating value from locally available biomass. A validated EnergyPlus model was used to assess shelter performance across representative Indian climates. The analysis considered roof heat transfer, indoor thermal conditions, Temperature–Humidity Index (THI), and the influence of key design parameters such as insulation thickness, sidewall height, shelter capacity, and cattle heat load. The thermal results were further connected to productivity-related economic benefits and greenhouse-gas mitigation. Crop-residue insulation substantially reduced roof-mediated heat gain and improved indoor thermal conditions, with annual THI reduction varying from 730 to 5826 THI·h across climates. Maximum reductions of 5.62 °C in indoor temperature and 4.18 THI units were observed. Pearl millet straw showed consistently better performance than paddy straw, while shelter geometry and insulation thickness were identified as important design factors. Beyond thermal performance, the intervention showed considerable potential for economic benefit, lower milk-related emission intensity, and avoided emissions from residue burning. The results highlight crop-residue insulation as a practical pathway linking resource recovery, passive cooling, climate resilience, and sustainable dairy production.