Search Results (1,033)

Precise estimation of lithium-ion battery State of Health (SOH) is highly demanded for reliable battery management systems, lifetime prediction, and safety assurance in electric vehicle and energy storage applications. Traditional data-driven approaches such as multilayer perceptron (MLP) often suffer from poor generalization and may produce non-physical degradation trends due to the absence of domain knowledge constraints. To address these limitations, this work proposes a monotonic Physics-Informed Residual MLP neural network framework for SOH estimation using the NASA battery dataset (B0005, B0006, B0007, and B0018). The proposed model incorporates a physics-based monotonic degradation constraint by penalizing positive gradients of SOH with respect to cycle index, thereby enforcing physically consistent capacity fade behavior. A loss function is employed to improve robustness and enhance late-cycle learning. Experimental results demonstrate that the proposed approach achieves an RMSE of 0.0287, MAE of 0.0181, and MAPE of 2.69%, indicating accurate and stable SOH prediction across multiple degradation patterns. The use of physics-informed constraints markedly enhances deterioration consistency and diminishes overfitting relative to solely data-driven models. The proposed structure offers a faithful solution for State of Health estimation in practical battery management systems. Full article

Gravimetric sorption testing is widely used to screen candidate sorbents for petroleum hydrocarbon remediation. Using a five-material Romanian sorbent screening as a case study, this study demonstrates why explicit mass-balance validation should be mandatory in such protocols. Natural zeolite, clay, peat, wood sawdust, and hops were tested against used motor oil and diesel fuel under a standardized protocol. A mass-balance check showed that results for peat, wood sawdust, and hops implied hydrocarbon uptakes exceeding the physically available mass; because the underlying raw records could not be located, these three datasets were excluded, illustrating the kind of error such validation is designed to catch. The validated result is a two-material comparison: for used motor oil and diesel fuel, respectively, clay and zeolite showed the higher sorption capacity (1.210 ± 0.016 g/g vs. 1.170 ± 0.010 g/g, Welch’s t-test, t(6.7) = −4.78, p = 0.0022; and 1.444 ± 0.005 g/g vs. 1.352 ± 0.031 g/g, t(4.2) = 6.51, p = 0.0023), a reversal consistent with, but not confirmed by, a pore-size- and viscosity-selective sorption hypothesis. Both materials showed low inter-replicate variability (CV < 2.3%), confirmed by Mann–Whitney U tests (p = 0.0079 for both hydrocarbons). Because measured capacities are three- to thirteen-fold higher than literature values for raw clinoptilolite and bentonite, these results are presented as protocol-specific bench-scale rankings rather than generalizable material properties. This study is offered as a worked example of validation-first data screening in gravimetric sorption testing; deeper characterization and re-measurement of the excluded materials are identified as next steps. Full article

The removal of phenol from contaminated effluents presents an industrial challenge owing to its toxicity at trace concentrations. Multi-walled carbon nanotubes (MWCNTs) have been studied as adsorbents for this purpose, given their high adsorption capacity and ease of separation. This work presents a theoretical phenomenological and numerical analysis of mass transport coupled to phenol adsorption on MWCNTs (external diameter $d_{ext}=50$ nm), parameterized using published experimental equilibrium data acquired under neutral pH conditions at 298 K. The mathematical model incorporates an effective pore diffusivity ($D_e=3.213\times {10}^{-10}$ m²/s) derived from pore structure parameters and describes three distinct scenarios: (1) pure physical adsorption via a modified Fick’s Second Law; (2) coupled diffusion–reaction with 0.5-order kinetics, herein treated as an empirical kinetic ansatz with phenomenological divergence from lumped empirical models (PFO/PSO); and (3) a parametric and sensitivity analysis on particle size (1–100 nm) and inlet concentration (1–5 mg/L). Numerical solutions confirm a Thiele modulus $\varphi \ll 1$ across the tested range, indicating a kinetically controlled regime with effectiveness factor $\eta \approx 1.0$, and validate the theoretical scaling $\varphi \propto C_s^{-0.25}$. During effluent polishing operations (reduction from 5 to 1 mg/L), the relative diffusive resistance increases by 49.5%, suggesting proportional increases in contact time or adsorbent dosage are required. A one-at-a-time (OAT) sensitivity analysis on $D_e$, $\kappa$, and $k_{obs}$ confirms that the kinetically controlled regime is preserved across plausible parameter ranges. The nanoscale architecture of MWCNTs reduces theoretical intraparticle diffusional resistance by several orders of magnitude relative to macroscopic granular adsorbents. We emphasize that these conclusions describe theoretical mass-transport advantages; experimental and pilot-scale validation under realistic, multi-component wastewater conditions remains an essential step before industrial deployment. Full article

This paper reports a research work on assessing clay content effect on the water retention capacity of clayey sandy soils. At first, experimental tests were conducted to measure the soil water retention curves (SWRCs) of clayey sandy soils. A total of four different soil samples, which have clay content of 0, 15, 30 and 50%, respectively, by total soil sample weight, were measured. Secondly, a revision of a physical–chemical (PC) analytical model previously proposed has been reviewed and adopted to represent the SWRC measurements and compared for its predictive performance against the classic van Genuchten model and the original PC model. The experimental results demonstrated that clay content has a significant influence on soil water retention capacity, showing that a positive correlation generally exists between them. The modelling results showed that the revised analytical model not only produced a good representation for the soil water retention curves over whole range of soil water content, particularly at low water content side, but also provided advanced insight into the fundamental physics underlying soil water retention mechanisms. The analysis of its parametric data highlights the functions of the involved physics and their roles shaping the SWRCs, which intrinsically relate to specific surface area, pore size distribution, and particle size and shape. At last, the model was used to describe the pore size distribution from a revised concept against conventional approach. However, the revised concept and approach needs further wide verification and is open for discussion. Full article

Food contaminants, including plasticizers, pesticide residues, heavy metals, and biotoxins, pose persistent risks to food quality and human health. Their diverse sources, complex migration pathways, and potential long-term toxicity make removal difficult. Conventional removal technologies, such as physical treatment, chemical degradation, adsorption, membrane separation, and biological methods, can reduce contaminant levels to varying degrees. However, they often show limited selectivity, matrix interference, harsh operating requirements, or losses of nutritional and functional components. Molecularly imprinted polymers (MIPs) are synthetic recognition materials with binding sites tailored to a target contaminant. Their template-induced cavities provide complementarity in size, shape, and functional-group arrangement, enabling selective adsorption in complex matrices. Recent studies apply MIPs to the enrichment, detection, and removal of plasticizers, pesticide residues, heavy metals, and biotoxins. Unlike recent surveys centered on MIP-assisted analysis and sensing, this review uses contaminant removal as the organizing problem and compares MIP-based strategies with conventional decontamination across four hazard classes. MIPs offer tunable selectivity, chemical stability, and reusability, but practical food applications still face template leakage, slow mass transfer, incomplete safety evaluation, matrix dependence, and scale-up limitations. Future work should prioritize green synthesis, surface imprinting, magnetic recovery, and systematic validation in real food matrices. To prevent analytical extraction from being conflated with remediation, the evidence is classified from proof-of-binding and analytical cleanup to edible-matrix treatment and process validation, and representative studies are compared using capacity, removal or recovery, equilibration time, selectivity, reuse, and matrix validation. Recent evidence also reveals substantial gaps for PFAS, microplastics, and nanoplastics: selective recognition is advancing, but food-safe removal remains largely unvalidated. Full article

Just energy transitions in the Global South unfold under conditions of institutional fragmentation, fiscal constraints, and high socio-ecological turbulence, making governance capacity a critical bottleneck for effective decarbonization and climate justice. This study proposes the Cybernetic Environmental Hub (CEH) framework, which extends the Viable System Model (VSM) to sustainability governance by integrating AIoT-enabled environmental monitoring, Early Warning Systems, decentralized data governance, and justice-centered institutional design. Methodologically, the article is primarily a conceptual framework paper accompanied by an illustrative single-site qualitative case study designed to probe the plausibility and diagnostic utility of the proposed architecture rather than to generate statistical generalization. The research combines theoretical development with participatory territorial diagnostics in the Caribbean Mining Corridor, where socio-ecological challenges were collected through participatory innovation workshops, thematically coded, and mapped onto the five VSM subsystems to identify systemic “variety gaps.” The analysis indicates that fragmented operational initiatives coexist with weak meta-systemic coordination, limiting adaptive capacity in energy transition processes. The CEH architecture is proposed to address these deficiencies by embedding AIoT sensing, federated learning, blockchain-based coordination, and Early Warning Systems within recursive governance structures and is grounded in a real cyber-physical deployment of around 90 monitoring stations across Albania, La Jagua de Ibirico and Algarrobo. The study also introduces a Territorial Governance Maturity Model (H1–H3) to diagnose systemic learning capacities and transition readiness across technological, institutional, data governance, and justice dimensions. The findings suggest that cybernetic environmental hubs may function as socio-technical infrastructures supporting coordinated, adaptive, and justice-centered energy transitions in the Global South, while comparative empirical evidence remains an agenda for future work. Full article

Background/Objectives: Non-communicable diseases (NCDs), including cardiovascular disease and diabetes, present an increasing challenge in humanitarian settings. While healthcare services in refugee camps have traditionally prioritised acute and communicable diseases, the long-term management of chronic conditions remains difficult, particularly among older adults. This study explored healthcare providers’ perspectives on barriers to cardiovascular disease and diabetes care among elderly Rohingya refugees living in Cox’s Bazar, Bangladesh. Methods: A qualitative study was conducted using semi-structured interviews with six physicians working in Rohingya refugee camps in Cox’s Bazar, Bangladesh. Participants were recruited through purposive and snowball sampling. Interviews were audio-recorded, transcribed verbatim, and analysed using inductive thematic analysis. Results: Three overarching categories of barriers were identified: service delivery barriers, physical and environmental barriers, and socio-cultural barriers. Healthcare providers described limited diagnostic capacity, restricted medication availability, fragmented continuity of care, transport difficulties, challenging camp terrain, language barriers, low health literacy, and reliance on informal healthcare providers as key challenges affecting long-term management of cardiovascular disease and diabetes. These interconnected barriers were perceived to reduce healthcare access and continuity of care for elderly Rohingya refugees. Conclusions: The findings suggest that humanitarian healthcare systems should strengthen long-term management of non-communicable diseases among ageing refugee populations. Improving diagnostic capacity, ensuring reliable access to essential medicines, enhancing continuity of care, and supporting culturally appropriate health education and outreach services may improve chronic disease management in humanitarian settings. Further research exploring the perspectives of elderly Rohingya refugees is needed to complement healthcare provider perspectives. Full article

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) evolution has been marked by the rapid accumulation of mutations, among which cytosine-to-uracil (C-to-U) transitions represent a major proportion of observed genomic changes. These mutations have been proposed to result from the activity of host APOBEC3 cytidine deaminases, innate immune enzymes capable of editing viral RNA. However, the molecular mechanisms underlying APOBEC3 involvement in SARS-CoV-2 biology remain poorly understood. Here, we systematically investigated physical interactions between APOBEC family members and the SARS-CoV-2 proteins using a Gaussia princeps protein complementation assay. Screening of APOBEC family proteins against the viral proteome identified specific interactions between APOBEC3G (A3G) and APOBEC3H (A3H) with the viral nucleocapsid (N) protein. These interactions were validated by co-immunoprecipitation and were found to be conserved across nucleocapsid proteins from all seven human coronaviruses, suggesting conserved structural determinants. Mechanistic analyses revealed that the RNA-binding and oligomerization capacities of A3G and A3H are key for their interaction with the SARS-CoV-2 nucleocapsid. Mapping experiments further showed that the C-terminal domain of N constitutes the minimal interacting region, with stronger binding observed in larger constructs encompassing adjacent regions, indicating cooperative stabilization. Further work will be needed to determine whether A3G and/or A3H can restrict viral replication and whether their interaction with the nucleocapsid allows them to access and mutate the viral genome. Full article

Decarbonisation targets are now widely adopted. However, the speed at which renewables are built depends on many factors, including technology, cost, regulation, and the decisions of investors, developers, and governments. Public acceptance is one of these factors, but it is not the decisive one. This article examines public acceptance specifically: the attitudes, perceptions, and preferences of citizens and consumers, which form only one part of the wider social acceptance of renewable energy that also involves policy-makers, intermediaries, and market actors. We ask how public acceptance of renewable energy varies across countries, what drives that variation, and which policy instruments most reliably turn public approval into built capacity. Using a structured narrative synthesis across 21 countries, we integrate major cross-national opinion surveys (Eurobarometer 555, the UNDP–Oxford Peoples’ Climate Vote 2024, Pew, DESNZ, CSIRO, KfW, Ipsos), peer-reviewed work on local community acceptance, and capacity data from IRENA and the IEA. Three findings stand out. Headline support is high almost everywhere—a median of 76% across 77 countries and 85% in the EU—but increasingly masks polarisation, most sharply in the United States, where the partisan gap on prioritising renewables widened from 26 to 49 points between 2020 and 2024. Technology-specific acceptance follows a broadly consistent order, from rooftop solar down through wind, geothermal, hydropower, and biomass to carbon capture and nuclear. And community acceptance turns less on physical proximity to infrastructure than on procedural fairness, place attachment, and meaningful financial participation. Our contribution is integrative: we propose operational indicators of public acceptance that travel across national contexts, a country typology linking acceptance institutions to renewable outcomes, and a five-element policy architecture—early participation, benefit sharing, procedural transparency, place-sensitive siting, and credible information—each mapped to the acceptance dimension it addresses. Full article

Dye wastewater pollution has become a critical environmental issue, while conventional silica adsorbents are often limited by high production costs and the use of chemical silicon sources. In this study, a black-liquor-derived silica-based adsorbent was prepared from bamboo alkaline black liquor through calcium hydroxide precipitation, acid leaching, washing, and calcination. The obtained product was systematically characterized by ICP-OES, XRD, SEM, and BET analysis. Phase analysis showed that the product was mainly composed of amorphous SiO₂, together with a small amount of residual CaSiO₃. The product was semi-quantitatively estimated to contain approximately 64.6 wt.% total SiO₂ and 14.7 wt.% total CaSiO₃, and the estimated SiO₂ yield was about 4.6 wt.% based on dry black liquor solids. The adsorbent exhibited a mesoporous structure with a specific surface area of 60.04 m²/g, a pore volume of 0.23 cm³/g, and an average pore diameter of 7.5 nm. The effects of adsorbent dosage, initial methylene blue concentration, solution pH, and temperature on adsorption performance were investigated. Under the selected conditions of 0.5 g adsorbent, 50 mg/L methylene blue, pH 7, and 20 °C, the removal efficiency reached 82.94%. Adsorption isotherm analysis showed that the Langmuir equation provided the best fit among the tested models over the studied concentration range, and the linear Langmuir fit gave an apparent capacity parameter of 840.3 mg/g. However, this fitted value should not be interpreted as a physically realizable ideal monolayer uptake. This work provides a feasible route for converting silicon-containing bamboo alkaline black liquor into a mesoporous silica-based adsorbent for dye removal. Full article

This study tailors titanium nitride (TiN)-reinforced RT70 HC nanocomposite phase change materials (PCMs) for medium-temperature thermal energy storage. TiN nanoparticles were incorporated into commercial RT70 HC at 0.1–2.0 wt.% by a two-stage method combining sodium dodecyl sulfate, magnetic stirring, and ultrasonication, and characterized by FT-IR, XRD, SEM-EDX, elemental mapping, DSC, thermal conductivity, Cp, TGA, and 1000-cycle tests. FT-IR and XRD confirmed the physical integration of TiN into RT70 HC without new chemical bonds or secondary phases, and SEM-EDX showed a concentration-dependent dispersion. The phase change temperatures were largely preserved. The latent heat varied non-monotonically with TiN content, increasing at low loadings (0.1–0.5 wt.%) and decreasing at higher loadings. Because each composition was prepared as a single batch and measured on small specimens, the low-loading latent-heat increase (up to about 9%) is indicative rather than statistically proven and may fall within the subsampling variance. The 0.5 wt.% sample reached the highest values of 306/296 J/g in the first cycle and 286/268 J/g after 1000 cycles. The thermal conductivity increased with TiN content, reaching a maximum enhancement of about 24.09% in the liquid phase (0.1785 to 0.2215 W/(m·K) at 80 °C) and 35.7% in the solid phase at 2.0 wt.%, whereas the specific heat capacity was lower at higher loadings, an indicative trade-off given the single-run measurement uncertainty. TGA showed degradation onset temperatures above 240 °C, a wide safety margin relative to the ~72 °C working range. Overall, the 0.1–0.5 wt.% formulations offered the most balanced thermal performance for medium-temperature applications. Full article

Finding cost-effective and eco-friendly ways to remove toxic heavy metals from wastewater remains a critical challenge for industrial sustainability. This study presents a comparative performance matrix of coal fly ash (CFA) and bone charcoal (BC) for the high-capacity remediation of Cd(II) and Pb(II) ions. This work establishes a direct cross-matrix comparison between a heterogeneous aluminosilicate phase (CFA) and a uniform calcium-phosphate structure (BC) under identical systemic boundaries. SEM/EDS, FT-IR, and complementary TG/DTG/DTA screenings confirmed that distinct material-specific functional frameworks drive a predominantly physical mechanism governed by electrostatic and van der Waals interactions. Equilibrium data fitted the non-linear Langmuir model well (R² > 0.99 at 20 °C). BC proved to be significantly more effective, achieving maximum sorption capacities (q_max of 397.55 mg/g for Pb(II) and 325.09 mg/g for Cd(II), outperforming CFA (118.22 and 105.59 mg/g, respectively). Sorption capacities decreased with temperature up to 80 °C, confirming the exothermic nature of the process, which was further substantiated by negative enthalpy values (∆H⁰ = −7.27 to −14.19 kJ/mol). Thermodynamic parameters indicated a spontaneous process (∆G⁰ < 0, −9.55 to −19.33 kJ/mol) with positive entropy changes (∆S⁰ = 5.82 to 39.09 J/(mol·K)). Adsorption kinetics followed the pseudo-second-order model, with intraparticle diffusion acting as a key rate-limiting step. Regardless of the adsorbent, Pb(II) ions were immobilized faster and more efficiently than Cd(II) due to a smaller hydration radius. In conclusion, both industrial by-products represent promising, sustainable options for heavy metal wastewater treatment, with BC demonstrating superior performance. Full article

This qualitative case study examines the feasibility of introducing transformative resilience in the informal settlement of Mohammad Amin Camp in Amman, Jordan. A participatory design approach was adopted, combining community workshops, focus-group discussions, semi-structured interviews, and spatial mapping to explain the urban challenges faced by people living in the camp. Themes associated with data collection were the need for social spaces, better waste management, and greater mobility. The findings suggest that community-defined resilience priorities extend beyond physical infrastructure improvements to include social interaction, accessibility, environmental quality, and local ownership, highlighting the value of participatory design in identifying context-specific resilience needs. Guided by the Enabling–Enacting–Envisioning framework, this study translated community knowledge into actionable urban design strategies, illustrating how participatory design can foster transformative resilience through integrated environmental, social, and governance interventions. Key interventions include the creation of green spaces, improved pedestrian pathways, community gathering spaces, and integrated waste management and fire safety measures. The proposed interventions address immediate urban challenges while strengthening long-term resilience through enhanced social cohesion, environmental adaptability, and community capacity. This study illustrates that participatory, small-scale spatial interventions can inform grounded and context-specific urban regeneration, offering practical insights for planners and policymakers working in informal settlement contexts. Full article

Battery engineers often work with repeated cycle-level monitoring signals that are related to ageing but are not direct capacity or resistance measurements. This paper studies how such a signal can be used to detect faster change and to decide whether the measured record is long enough to locate the transition reliably. We analyse 14 lithium-ion cell records from a processed Hawaii Natural Energy Institute (HNEI) cycle-level table, using the charging-to-discharge duration ratio (C/D) as a practical charge/discharge-duration indicator. The corresponding original HNEI measurement files were checked to improve the cell description and to examine whether a capacity-based comparison was possible. They confirm substantial capacity fade, but they also contain non-physical capacity entries near cycle 370; these entries are not used as validation of C/D transition cycles. We compare a linear reference, broken-line regression, a smoothing-spline curvature check, an aggregate Bayesian smooth-transition model, and a battery-level hierarchical Bayesian model. The hierarchical model estimates a mean battery-level transition cycle of 553.9 cycles (95% credible interval: 547.1–560.9), with substantial battery-to-battery variation (standard deviation about 142 cycles). All 14 batteries show a positive increase in the rate of change of C/D. Randomly removing about half of the measurements while retaining the full test span widens uncertainty but preserves the acceleration conclusion and nearly preserves battery ordering. In contrast, cutting off the late part of the test record strongly destabilizes transition timing and extrapolation. The approach is therefore useful as a retrospective screening and test-interpretation tool for a chosen ageing-related signal. It is not a direct capacity-knee detector, a mechanism diagnosis, or a remaining-useful-life predictor. Full article

Environmental impacts of wastewater from textile and dyestuff industries are of growing concern due to limited freshwater availability and inadequate treatment facilities. Carbon nanotubes (CNTs) offer excellent adsorption potential because of their outstanding mechanical and chemical properties, however; their application in textile effluent treatment has not been widely studied. Moreover, laboratory-based studies are often costly and limited to a few variables, making it challenging to reveal the underlying relationships among several physicochemical parameters and CNT treatments. Multivariate statistical analysis (MVSA) offers an effective approach to overcome this challenge. To the best of our knowledge, no studies have integrated laboratory analysis of nanotube-based textile effluent treatment with an MVSA approach. This study aims to evaluate the physicochemical characterization of textile effluent, treat effluent with CNT, and explore the relationship between physicochemical parameters and CNT by integrating laboratory experiments with MVSA. For this purpose, single-walled CNT (SWCNT) and multi-walled CNT (MWCNT) were applied in batch mode adsorption experiments using various dosages and adsorption times. Scanning electron microscopy and Fourier transform infrared spectroscopy (FTIR), along with physicochemical analyses, were conducted to characterize the effluent and adsorption processes. The FTIR spectrum indicated that the absorption peaks of C=C, C=O, and the acidic f -OH group on CNTs enhance wettability and hydrophilic character, thereby increasing adsorption capacity. Experimental results demonstrated significant reductions in electrical conductivity (EC), total dissolved solids (TDS), turbidity, total organic carbon (TOC), and chemical oxygen demand (COD). CNT dosages of 1 to 5 g/100 mL and adsorption times of 2 to 5 h achieved removal efficiencies ranging from approximately 20% to 90% for SWCNT and MWCNT. MVSA indicated that MWCNT was more strongly associated with ionic and physical parameters (turbidity, TDS, EC, and pH), whereas SWCNT was more strongly related to organic load indicators, particularly COD and TOC. Overall, this study highlights the potential of CNT coupled with the MVSA technique as an effective and sustainable approach for textile wastewater treatment and offers valuable insights for researchers working in this field. Full article