Radio frequency identification (RFID) technology is a key enabler of the green Internet of Things (IoT). In recent years, green RFID systems have emerged as sustainable solutions to support various IoT applications that require low-power and cost-effective devices while minimizing environmental impact. However, most existing passive ultra high frequency (UHF) RFID tags operating in the 860 MHz-960 MHz band largely rely on metallic conductors and polymeric substrates, such as copper, aluminum, silver, and polyimide, for antenna fabrication. The use of these materials limits the sustainability of the resulting devices. Therefore, there is a growing need to develop eco-friendly and high-performance tag antennas that meet the requirements of future sustainable digital technologies. In this work, a folded dipole antenna is fabricated using a highly conductive graphene sheet on three different paper substrates, including pristine paper (PP) and two in-house manufactured planarized paper substrates with different surface finishing. The antenna has been designed and realized based on measured properties of graphene sheet and paper substrates that helps to achieve a long read range compared to existing graphene sheet and paper based UHF tag. The performance of three developed graphene sheet-based tags is evaluated by attaching them to cardboard and plastic boxes and measuring their response using a fixed-position JADAK ThingMagic M6e RFID reader. The developed tag based on PP demonstrates the highest read range of up to 7.2 m when attached to the cardboard in an indoor environment, while the tag realized using planarized paper substrates with different surface finishing achieves read ranges of 4.8 m and 4.2 m.
The growing reliance on fishmeal in aquaculture is increasingly unsustainable due to rising costs, ecological concerns and market competition, emphasizing the need for alternative protein sources. Eisenia fetida , a non-conventional protein-rich earthworm, offers promising nutritional values including high levels of protein, lipids, sugars, and vitamins while thriving on various organic substrates. The Corner Brook Pulp and Paper Mill generates substantial quantities of carbon-rich pulp and paper mill sludge (PPMS), which is typically incinerated without considering alternate uses. To address this problem, this study investigated the use of PPMS as a substrate for cultivating E. fetida and systematically compared the biochemical profile changes of earthworms to provide insights into their potential use/utilization as a sustainable alternative protein source for poultry and aquaculture feeds. E. fetida were cultured in varying PPMS-to-cow manure ratios (100%, 75%, 67%, 60%, 50%, and 0%) to modulate carbon content and optimize growth, and were analyzed for total phenolic content (TPC), total antioxidant activity (TAA), total soluble protein, lipids, soluble sugar, vitamins, and mineral content. Statistically-significant differences ( p < 0.05) were observed across all treatments. The highest total soluble protein content was found in the cow manure (TS-0), with soluble protein content decreasing as the PPMS proportion increased. Conversely, E. fetida cultured in PPMS (TS-100) exhibited higher levels of TPC, TAA, total lipids, sugar, and water-soluble vitamins B _1 , B _2 , B _3 , B _5 , B _6 , K _2 and K _3 , surpassing those reported in other alternative fishmeal sources. Total essential macro, micro, and non-essential heavy metals were significantly higher ( p < 0.05) in TS-0 except for arsenic (TS-100). Based on their biochemical composition, E. fetida cultured on 50–67% of PPMS exhibit a nutrient profile indicative of their potential use as a protein source for aquaculture and poultry, with this range suggesting a potentially cost-effective substrate proportion for PPMS valorization and reduced accumulation of potentially toxic elements (PTE), while future controlled feeding trials are warranted to confirm their nutritional value and digestibility.
This roadmap on biotechnology for wastewater treatment aims to cover some of the most recent advances in the field of environmental biotechnologies that have been applied for domestic wastewater treatment processes, and emphasizes (i) resource recovery, (ii) energy production and conservation, (iii) removal of contaminants of emerging concerns (CECs), (iv) decentralized wastewater and agricultural wastewater treatments, all in the context with the current challenges presented. The roadmap is comprised of multiple sections, each of which introduces the fundamentals of biological process, efficient nutrients removal/recovery, energy production by microbial fuel cells (MFCs), greenhouse gas (GHG) emission and control, various treatment biotechnologies for non-point source wastewater treatment, alternative biological pathways for energy conservation or production, bioremediation of freshwater and groundwater, and advanced molecular tools applied in wastewater treatment. The status of each field, the current and future challenges, and a perspective of the required advances needed to tackle these challenges are presented. This article is not meant to be a fully comprehensive review but rather an up-to-date snapshot of different areas of research, with a minimal number of references that focus upon the very latest research developments.
Worldwide demand for lithium (Li) is surging due to the increased consumption for Li-ion batteries that power electric vehicles, portable electronics, and grid energy storage. This demand has driven research pursuits in Li extraction to enable improvements in extraction from both established sources and unconventional resources (e.g. geothermal brine, produced water, and seawater). One route for direct extraction of Li ^+ from aqueous solutions is to capture and release the cation using intercalation material hosts. An example material is iron phosphate (FP), which has previously demonstrated high selectivity for capturing Li ^+ relative to other cations commonly found in brines. The process reported herein specifically drives Li ^+ into FP via a chemical redox process. In this work, results will be described to suggest that selectivity toward capture of Li ^+ from brine into FP is dependent on the pH of the surrounding solution. It was found that reducing the solution pH from 7.5 to 4.2 increased the selectivity of the FP to capturing Li ^+ as opposed to Na ^+ , where the improvement was over an order of magnitude in selectivity for decreased pH brine conditions. Coating titania on the FP was also investigated and demonstrated improved Li ^+ selectivity. The optimized combination of titania coating and lower pH resulted in a selectivity for Li ^+ over Na ^+ capture of over 2,000, while the baseline at pH 7.5 that did not have titania coated particles was 112. This work provides additional insights into material considerations and process conditions that influence Li ^+ selectivity over Na ^+ during chemical redox-driven Li ^+ extraction using an intercalation host.
Compound semiconductors are increasingly used in electronics and optoelectronic technologies, but their manufacturing, processing, and disposal have significant environmental and resource costs. As the worldwide demand for photonics, sensing, and high-performance electronic devices grows, sustainability and circularity have evolved from peripheral issues to structural constraints. This Topical Review covers recent breakthroughs in materials design, semiconductor production, electronic waste recycling, and life-cycle assessment for optoelectronic and compound semiconductor systems. Emerging solutions such as organic and biodegradable electronics, recyclable substrates, and dissolvable packaging are compared to process-level efforts to lower energy, water, and chemical intensity during manufacturing. Crucially, examination of real-world e-waste systems demonstrates that end-of-life recovery of III–V semiconductors remain highly limited under current industrial recycling infrastructures, resulting in substantial dissipation of critical elements across waste streams. The issues addressed in this review align directly with UN Sustainable Development Goals SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action), with proposed strategies intended to advance circularity and resource efficiency without compromising broader development objectives. These findings indicate that meaningful circularity must be achieved primarily through upstream material selection, process redesign, and life-cycle-informed system strategies, rather than relying on downstream recycling alone.
Automated detection of anthropogenic surface features from high-resolution imagery is an emerging area in Earth and environmental sciences. It supports renewable energy assessment, development planning, and the monitoring of sustainable practices. The increasing capabilities of AI and the availability of unrestricted, high-resolution imagery in public domains open new vistas for intelligent systems to remotely track infrastructure rollout. Monitoring of solar energy infrastructure is pivotal to meeting the UAE Consensus of tripling renewable energy capacity. In this study, we developed and evaluated deep learning-based object detection models to identify rooftop solar panels from high-resolution Google Earth imagery. A total of 1000 images from Trivandrum (India) and Sydney (Australia) were used to train and compare YOLOv5, YOLOv8, YOLOv11, and Faster R-CNN models. Among the evaluated models, YOLOv11 achieved the strongest overall performance, recording a precision of 0.917, a recall of 0.908, mAP@50 of 0.958, and mAP@50–95 of 0.559 in Trivandrum, and a precision of 0.756, a recall of 0.817, mAP@50 of 0.844, and mAP@50–95 of 0.677 in Sydney. The results suggest that AI-based approaches can significantly enhance large-scale mapping of solar panel installation, contributing to transparent, evidence-based energy policy, infrastructure planning, and sustainability assessment.
The integration of wireless, battery-free sensor tags into wood wool lightweight boards for smart construction is presented. The tags consist of insulating paper substrates with printed conductors including a sensor and an antenna, as well as a commercial radio frequency identification sensor chip. The embedded tags are intended to enable monitoring of moisture ingress within construction elements, supporting condition-based maintenance and building monitoring. Experimental results show that printed tags fabricated on insulating kraft paper achieve signal strengths comparable to commercial PCB-based tags, indicating no compromise in wireless readout performance. Accelerated life testing under constant damp heat conditions demonstrates stable operation over the test duration corresponding to an equivalent service life exceeding 7.5 years, despite exposure to highly alkaline cementitious environments during board fabrication. Humidity characterization confirms a stable and repeatable sensor response between 20% and 60% relative humidity. A cradle-to-gate life cycle assessment focused on global warming potential reveals that the integration of a printed sensor tag increases the total environmental impact of the wood wool board by only 2%. The results demonstrate that printed electronics enables durable and reliable smart functionality in construction materials while introducing only a small environmental burden, supporting their suitability for large-scale deployment in sustainable buildings. This work contributes to the United Nations Sustainable Development Goals (SDGs), particularly SDG 9 (Industry, Innovation and Infrastructure) and SDG 12 (Responsible Consumption and Production), by combining smart infrastructure monitoring with resource-efficient additive manufacturing and environmentally conscious material selection.
Decarbonizing the air-transportation sector remains one of the most difficult barriers to mitigating climate change. Sustainable aviation fuels (SAF) are drop-in jet-fuel alternatives that has the potential achieving substantial reductions in environmental footprint without requiring major changes to existing aircraft or fueling infrastructure. In this work, we develop an open-source Python modeling platform for probabilistic techno-economic analysis of SAF production via alcohol-to-jet (ATJ) pathways from bioethanol. The minimum jet selling price for the baseline biorefinery is 8.89 USD gal ^−1 , primarily driven by the bioethanol feedstock price. Additional important costs are associated with delivering and storing hydrogen gas. Mote Carlo simulation incorporating both process and market uncertainty shows that the SAF selling price ranges widely from 5.0–13.8 USD gal ^−1 under a varying set of design and economic assumptions. TEA results highlight critical research and development priorities, including cost-effective bioethanol production and improved hydrogen integration strategies. These systems analyses indicate that decentralizing ATJ biorefineries, particularly through retrofitting existing corn-ethanol facilities and integrating off-site hydrogen production with carbon capture and storage, offers practical routes to enhance economic robustness and accelerate the commercial deployment of cost-competitive SAF.
Water is intrinsically entwined with multiple elements such as energy, food, resource (otherwise waste), greenhouse gas emissions, ecosystem, and land system, which make these complex interdependencies challengeable to manage at different regions and scales. This roadmap charts actionable pathways toward water-positive, waste-zero, and net-zero–aligned water resource management by treating water services as a coupled water–energy–waste–carbon system rather than a stand-alone infrastructure sector. It synthesizes current status, binding constraints, and deployment-ready interventions across treatment, reuse, resource recovery, agriculture, watershed management, and digital decision-support, with attention to technology readiness, institutional capacity, and regional feasibility. The roadmap emphasizes that many failures in water sustainability are not primarily technical but arise from governance, incentives, legitimacy, and data quality, and therefore integrates social-science insights on public acceptance, equity, and workforce development alongside engineering solutions. To move beyond aspirational recommendations, it proposes an auditable measurement framework, minimum indicators for water efficiency and security, energy intensity, greenhouse-gas emissions, materials circularity, and service equity, and outlines a phased implementation pathway (near-, mid-, and long-term) with stakeholder responsibilities and monitoring, reporting, and verification requirements. It also highlights cross-sector risks and trade-offs, including climate-driven volatility, emerging contaminants, cybersecurity, and the potential for burden shifting between water and energy decarbonization. By providing a systems-oriented structure for prioritization, scenario evaluation, and accountable deployment, this roadmap is intended to support policymakers, utilities, basin authorities, financiers, and researchers in designing credible, context-specific strategies that deliver resilient water services while advancing circular economy and climate goals.
Accurate prediction of air pollutant concentrations is essential for environmental policy, public health protection, and early warning systems. In recent years, artificial intelligence (AI) techniques, including machine learning (ML), deep learning (DL), and hybrid models, have significantly improved the forecasting of particulate matter, gaseous pollutants, and real-time Air Quality Index (AQI). This study presents a systematic review of AI applications for predicting air pollutant concentrations, with emphasis on comparing global and Nigeria-specific studies. To ensure methodological consistency and avoid inappropriate comparisons, the analysis was structured according to modelling task: station-level particulate-matter forecasting, satellite-based particulate-matter mapping, and AQI prediction. Results were interpreted descriptively within each category because of differences in datasets, validation strategies, and prediction objectives. Findings show that data availability and monitoring infrastructure strongly influenced model selection and reported performance. Global studies commonly apply more complex ML, DL, and hybrid models using long-term multi-station and multi-source datasets, whereas Nigeria-specific studies rely more on simpler ML approaches because of limited data availability and sparse monitoring networks. Differences in reported performances are therefore linked mainly to data conditions and validation methodology rather than the inherent superiority of any specific algorithm. The study highlights the importance of aligning modelling approaches with available data, improving validation practices, and expanding monitoring infrastructure in data-limited regions. It emphasizes the value of context-specific modelling frameworks and multi-source data integration for evidence-based environmental decision-making. Beyond synthesis of existing literature, the study further contributes to a context-aware analytical framework for interpreting and applying AI models for air-quality prediction.
Glycerol hydrogenolysis represents an important route for biomass valorization, enabling the sustainable conversion of glycerol, a major byproduct of biodiesel production, into value-added chemicals and thereby improving the utilization of renewable carbon resources. Selective hydrogenolysis of glycerol to 1,3-propanediol represents a critical challenge, as high selectivity requires precise control over competing C–O bond cleavage pathways on bifunctional catalysts. In this work, we establish an active-site-resolved kinetic framework to quantitatively elucidate the respective roles of metal, Brønsted acid, and Lewis acid sites in governing glycerol hydrogenolysis over Pt-based mesoporous catalysts. By systematically decoupling metal and acid site densities while preserving catalyst morphology and transport characteristics, intrinsic reaction rates and turnover frequencies for individual reaction pathways are directly quantified. Rate analysis reveals that both 1,3-propanediol formation and over-hydrogenolysis to 1-propanol are metal-mediated but are distinctly modulated by acid functionality: moderate Brønsted acidity selectively promotes 1,3-propanediol formation, whereas excessive Brønsted acidity shifts selectivity toward undesired pathways, while Lewis acid sites alone exhibit minimal promotional effects. The 0.2 wt% Pt/W–KIT-6 catalyst with a Pt site density of 9.7 × 10 ^−6 kmol kg $^ {-1}_{\mathrm{cat}}$ , a Brønsted acid-site density of 0.27 × 10 ^−6 kmol kg $^ {-1}_{\mathrm{cat}}$ , and a Lewis acid-site density of 0.06 × 10 ^−6 kmol kg $^ {-1}_{\mathrm{cat}}$ exhibits approximately 12% glycerol conversion with ∼80% selectivity to 1,3-propanediol under 200 ^∘ C and 3 MPa H _2 . The Pt-site-normalized turnover frequency for the selective glycerol-to-1,3-PDO pathway is approximately 0.07 s ^−1 . Turnover frequency analysis further demonstrates that Brønsted acid sites act as true kinetic promoters rather than merely increasing apparent activity through surface coverage effects. These findings provide a mechanistically rigorous description of metal–acid cooperation in glycerol hydrogenolysis and establish a generalizable kinetic strategy for resolving active-site contributions in complex bifunctional catalytic systems, extending beyond glycerol valorization to selective hydrogenolysis and upgrading reactions involving multifunctional oxygenates.
A dye-sensitized solar cell (DSSC) employing a Titania (TiO _₂ ) photoanode sensitized with N3 dye was integrated with a carbon-based supercapacitor to form a three-electrode photo-supercapacitor device. In this architecture, a double-sided carbon-based electrode functions as a dual-purpose intermediate electrode, serving simultaneously as the counter electrode for the DSSC: facilitating efficient redox electrolyte regeneration and as an electrode in an electric double-layer capacitor for energy storage. Four commercially available carbon-based materials, namely, graphite, activated carbon, mesoporous carbon (MC), and graphene, were systematically evaluated as intermediate electrodes to assess their influence on device performance. Prior to integration, the standalone DSSC and supercapacitor components were characterized independently. The DSSC achieved a maximum power conversion efficiency (PCE) of 3.26% when graphite was used as the counter electrode, while the supercapacitor exhibited a maximum specific capacitance of 43.7 F g ^−1 with MC electrodes. The integrated photo-supercapacitor device was subsequently evaluated under simulated solar illumination (100 mW cm ^−2 ) using constant-current and constant-voltage charging protocols. The integrated system delivered a maximum PCE of 3.10% and a specific capacitance of 40.0 F g ^−1 when MC was employed as the intermediate electrode, demonstrating effective photo-charging behavior and energy storage capability. These results highlight the viability of using commercially available carbon materials as multifunctional intermediate electrodes for practical and scalable dye-sensitized photo-supercapacitor systems.
Methane (CH _4 ), is a potent greenhouse gas released from a wide range of natural and anthropogenic sources. This study explores the performance of thermal catalysis for CH _4 abatement under dilute conditions (10–500 ppm), using a fixed-bed reactor operated at a total flow rate of 100 ml min ^−1 . A matrix of catalysts comprising of 1 wt% Pd, Ni, and Ag metals supported on CeO _2 , TiO _2 , and Al _2 O _3 was synthesized and evaluated under identical reactor conditions to enable comparative assessment of catalyst performance for CH _4 oxidation. In particular, 1 wt% Pd/CeO _2 presented the highest activity across all concentrations, achieving complete conversion ( $ \gt 95$ %) below 500 ${\,}^\circ$ C at 500 ppm down to 10 ppm of CH _4 , for the flow rate considered in this study. It also demonstrated low onset temperatures (180 ${\,}^\circ$ C–200 ${\,}^\circ$ C) at 10 ppm and comparatively low apparent activation energies (45 kJ mol ^−1 ), indicating high catalytic activity and energy efficiency ( $1.8\times10^{-3}$ kWh g ^−1 CO _2 e at GWP _20 ) due to reduced heating requirements to initiate methane conversion. In contrast, Ag- and Ni-based catalysts, particularly when supported on Al _2 O _3 , demonstrated promising activity at higher temperatures, over 80% conversion at 800 ${\,}^\circ$ C and onset temperatures at 400 ${\,}^\circ$ C–580 ${\,}^\circ$ C for 10 ppm, for 100 ml min ^−1 considered in this study. Activation energies ranging from 10–80 kJ mol ^−1 and 75–110 kJ mol ^−1 at 10–500 ppm have been calculated for Ag/Al _2 O _3 and Ni/Al _2 O _3 , respectively. While Pd/CeO _2 achieves superior CH _4 conversion, its high life-cycle emissions reduce its relative climate advantage, yielding net CO _2 e emission rates (−0.0166 gCO _2 e s ^−1 at 500 ppm and GWP _20 ) comparable to Ag/TiO _2 (−0.0164 gCO _2 e s ^−1 ) and Ni/CeO _2 (−0.0145 gCO _2 e s ^−1 ), assuming specific catalyst area and operating time. These results demonstrate that catalysts with lower catalytic activity can achieve comparable net climate benefit when energy demand and embodied emissions are included, highlighting the importance of life-cycle metrics and material impact for scalable, low-concentration CH _4 mitigation strategies.
In this study, an attributional life cycle assessment (LCA) and techno-economic analysis (TEA) were used to determine the performance of a seaweed biorefinery producing a biopolymer film integrated with biochar carbon capture and storage. In the LCA, a total of 72 scenarios were modelled, investigating the system’s sensitivity to various plant production scales, energy mixes, product uses, and end-of-life pathways. The system was not particularly sensitive to plant production scale, though the application of a green energy mix afforded a $\sim $ 40% reduction in overall greenhouse gas emissions. Cradle-to-gate impacts ranged from −3.71 to −2.17 kg CO _2 eq kg ^−1 when the biochar route was included, as opposed to −1.30 to −2.43 kg CO _2 eq kg ^−1 without. When also accounting for the end-of-life disposal of the polymer, cradle-to-grave impacts remained extremely low, ranging from −3.64 to −1.24 kg CO _2 eq kg ^−1 (anaerobic digestion), −2.27–0.14 kg CO _2 eq kg ^−1 (incineration), and −1.82–0.59 kg CO _2 eq kg ^−1 (composting). However, if the polymer was disposed of in landfill, this increased to between 2.99 and 5.40 kg CO _2 eq kg ^−1 . The results from TEA showed that at an operational scale of 10 000 t yr ^−1 , the optimal biopolymer selling price was $9.36/kg (USD) for a seaweed price of $2000/t, although this was reduced to $4.09/kg (USD) if the seaweed price was reduced to $500/t. Interestingly, including the cost of biochar production only increased the price of the biopolymer by between 1% and 4% at this scale. Therefore, producing biopolymers from seaweed is not only economically comparable to alternative biopolymer production methods, but when combined with a biochar carbon capture process, it becomes a highly cost-effective way to reduce the environmental impact of biopolymer manufacturing.
Limiting global warming to 1.5 °C or 2 °C requires deep decarbonization across energy, economic, and behavioral systems. While hybrid modeling frameworks combining top–down computable general equilibrium (CGE) models with bottom–up energy system models (e.g. TIMES) are well-established, few studies have integrated large-scale behavioral disruptions or quantified their indirect, economy-wide rebound effects. This study addresses this gap by soft-linking a CGE and TIMES model to evaluate the consequences of a 20% reduction in private vehicle demand in Quebec, Canada, a scenario consistent with regional sustainable mobility policies and empirical evidence on car-sharing. The analysis examines key indicators, including greenhouse gas (GHG) emissions, sector-specific energy consumption, and economic metrics like GDP, household incomes, and investments. Results show that behavioral disruption can be a win–win measure, improving economic performance while reducing decarbonization costs. The linked framework reveals sectoral reallocations, with industrial emissions declining and service-sector emissions partially increasing, reflecting rebound effects that evolve over time—from 17% in 2025% to 67% in 2050—consistent with transport rebound effects reported in the literature (16%–92%). Energy savings remain substantial, particularly for fossil fuels, with transportation energy use decreasing by 4%–10% relative to the baseline of 461.6 PJ of which private vehicles accounted for 44.4% in 2021, though increased low-carbon electricity consumption moderates long-term GHG reductions. This study highlights the importance of incorporating behavioral dynamics and rebound effects into prospective decarbonization modeling. It contributes to life cycle systems thinking and provides critical insights for policymakers designing robust, demand-side climate strategies.
Phosphorus (P) is a finite, essential resource critical for agriculture, yet its unsustainable management through excessive fertilizer application leads to significant environmental degradation, including water pollution. This directly impedes progress towards UN Sustainable Development Goals (SDGs) 2 (Zero Hunger) by wasting resources vital for food security and SDG 6 (Clean Water and Sanitation) by polluting aquatic ecosystems. Here, we develop a reagent-free orthophosphate chemosensor based on a sorbent material containing graphene oxide (GO) and diallyl-dimethylammonium chloride (PolyDADMAC), termed GO-PDDA. Laser-induced graphene electrodes were coated with GO-PDDA material using four different grafting or drop-cast techniques. Electrochemical testing showed that grafted GO-PDDA electrodes were more efficient than drop-cast GO-PDDA or DADMAC grafted electrodes. Developed GO-PDDA sensor is applied for sensing orthophosphate in aqueous samples at pH 7–9. Equivalent circuit modeling indicated that capacitive behavior coincides with Frumkin/Melik-Gaykazyan adsorption theory, where tetrahedral oxyanions increase low-frequency capacitance in thin films. The sensor achieves a detection limit of 20 ± 4 ppb with a rapid 5 min response time, covering concentrations relevant to natural waters. It exhibits high selectivity, being 97% selective for divalent ortho-P over common interferents, 93% over chloride/nitrate, and 87% over sulfate. The chemosensor is reusable, showing less than 5% performance change after regeneration. Validated against EPA Method 365.3 in urban creek water ( R ^2 = 0.92), it offers a faster, reagent-free alternative for direct ortho-P quantification. This work marks the first use of PolyDADMAC as a recognition material in an electrochemical sensor, positioning it as a promising tool for sustainable P management, directly supporting SDG 2, SDG 6, and SDG 12 (Responsible Consumption and Production).
To meet the demands of exponential population growth, industrial and agricultural activities have intensified, resulting in the release of numerous hazardous substances, including emerging contaminants (ECs). Such chemicals include pharmaceuticals, emerging pathogens, pesticides, industrial chemicals, and microplastics. ECs are persistent in various environments, difficult to remove during wastewater treatment, and their elimination has become of global concern. In fact, the mitigation of ECs aligns with some of the United Nations Sustainable Development Goals (SDGs), such as SDG 6, SDG 11, SDG 12, SDG 13, and SDG 14 which are related to minimizing hazardous chemicals in water bodies, management of waste through its life cycle and the conservation of water resources for sustainable development. One promising approach is the ‘waste-by-waste’ strategy, which adopts a circular economy perspective by repurposing residues from industrial, agricultural, and domestic sources to remove ECs. Such compounds are usually degraded by oxireductases, especially laccases, which oxidize ECs, reducing the toxicity of the pollutants and their intermediates. These enzymes can be immobilized in waste-derived biochar, enhancing catalytic performance and system reusability in environmental remediation, representing a sustainable and cost-effective alternative for ECs degradation. This review investigates the potential of waste-derived biochar for enzyme immobilization and its application in ECs mitigation. It highlights the principles of waste-by-waste treatment and the circular bioeconomy, outlines methods of biochar production and enzymatic immobilization, and critically discusses recent advances as well as the main challenges of this emerging approach.
The development of non-flammable, non-volatile electrolytes is important for safer lithium and sodium batteries, to facilitate our transition to a net zero economy, but the reliance on fluorine-containing anions brings significant environmental concerns. Here, acesulfamate based ionic liquids (ILs), sodium acesulfamate (Na[ace]) and lithium acesulfamate (Li[ace]) are introduced as promising new fluorine free materials as a more sustainable alternative to the perfluorinated anions (BF _4 , PF _6 , FSI and TFSI) currently utilised in energy storage devices. The resulting ammonium, phosphonium and pyrrolidinium acesulfame ILs showed promising physicochemical properties with a relative high conductivity (e.g. 1.4 × 10 ^−4 S cm ^−1 at 30 °C for [N _1222 ][ace]) and a wide electrochemical stability window (∼4 V). Lithium and sodium acesulfamate salts were synthesised from potassium acesulfamate (ACE-K) via an acid-base reaction. The use of acesulfamate ILs as electrolytes was investigated by mixing with sodium or lithium acesulfamate salts and characterisation of their physicochemical and electrochemical properties. Polyethylene oxide based free standing membranes composed of [N _2222 ][ace] with Na or Li [ace] were fabricated and tested in symmetrical Li or Na metal cells, demonstrating good electrochemical performance in both variable current density tests and longer-term cycling at elevated temperatures. Thus, the new Li and acesulfamate salts, the ILs and their mixtures, represent valuable new materials for the development of fluorine free electrolytes for energy storage devices.
