Search Results (204)

Infrared-selective stealth films, which concurrently offer high visible transmittance, suppressed infrared emission, and selective thermal dissipation, have emerged as compelling candidates for infrared protection and thermal-target stealth. However, traditional multilayer architectures are predominantly designed through empirical trial-and-error protocols, which inherently hinder the synergistic optimisation of multiband spectral performance and yield suboptimal parameter-tuning efficiency. To circumvent this bottleneck, we introduce a reinforcement learning (RL)-driven multi-objective optimisation framework that automates the design of composite thin-film configurations. The optimised multilayer film structure consists of TiO₂/ITO/Ag/ZnO/SiO₂, with layer thicknesses of 180, 656, 10, 33.75 and 50 nm, respectively. Spectral characterisation reveals a weighted average visible transmittance of 79.77% over the 0.38–0.78 μm range, alongside blackbody-weighted average emissivities of 33.93%, 72.93%, and 19.94% in the 3–5, 5–8, and 8–14 μm bands, respectively. Consequently, the spectral profile exhibits high visible transparency, deep suppression of emissivity within the atmospheric windows (3–5 and 8–14 μm), and markedly elevated emissivity in the non-atmospheric band (5–8 μm). Analysis of the electromagnetic field distribution and power-loss density along the thickness direction reveals that the energy transmission and dissipation behaviours across distinct bands are synergistically governed by multilayer interference, interfacial multiple reflections, and lossy interlayer coupling mechanisms. Furthermore, angle-resolved infrared-emissivity analysis calibrated against the normal-incidence FDTD spectrum confirms that the structure retains robust polarisation adaptability and pronounced spectral selectivity at incidence angles up to 80°. The above results demonstrate the effectiveness of the reinforcement learning-driven optimisation framework for the automated co-design of multiband spectral responses. Moreover, the uncovered multilayer interference and loss-coupling mechanisms furnish a solid physical foundation for further performance refinement and rational design of transparent stealth coatings. Full article

Selective recovery of gold from cyanide leach liquors is hindered by the co-dissolution of iron minerals that generate ferrocyanide complexes which strongly compete with [Au(CN)₂]⁻ at ion-exchange sorbents. Here, we investigate mixed-bed interpolymer systems (IPS) composed of a strong-acid sulfonated polystyrene–divinylbenzene cation exchanger (TC007, Na⁺ form) and a strong-base quaternary ammonium anion exchanger (AV-17-8, Cl⁻ form) as a charge-selective platform for gold cyanide recovery. IPS compositions spanning cation-to-anion molar ratios from 6:0 to 0:6 were evaluated in batch contact with binary model solutions containing 30 mg L⁻¹ each of [Au(CN)₂]⁻ and [Fe(CN)₆]⁴⁻ at pH 10 and 25 °C. The optimal 1:5 IPS achieved an [Au(CN)₂]⁻ extraction degree of 79.88% and a selectivity coefficient β = D_Au/D_Fe = 4.95 at 48 h, whereas the pure AV-17-8 anion exchanger (0:6) reached only 37.55% Au extraction at 48 h, following an atypical delayed-uptake kinetic profile rather than the rapid, near-quantitative capture expected of an unmodified strong-base resin. Sorption kinetics were best described by a pseudo-second-order model (R² = 0.9992), confirming ion exchange at quaternary ammonium sites as the dominant rate-controlling step, with a ~30-fold increase in k₂ for [Au(CN)₂]⁻ in the 1:5 IPS relative to AV-17-8 alone. FTIR spectroscopy and TGA-DSC revealed the incorporation of metal cyanide complexes into the IPS matrix, with diagnostic C≡N stretching bands at 2108.7 and 2034.1 cm⁻¹ and an additional thermal event at 200–280 °C. These findings establish compositionally tunable IPS based on commercially available resins as a charge-selective sorbent platform demonstrating a capacity to regenerate under single-cycle elution conditions for gold cyanide recovery from ferrocyanide-containing process streams while highlighting the need for further evaluation under industrial Fe:Au ratios. Full article

Electromagnetic interference (EMI) shielding is essential in modern electronics, telecommunications, and aerospace systems. Graphite-based materials are promising shielding candidates due to their tunable electrical conductivity, low density, corrosion resistance, and thermal stability. However, numerical studies investigating the combined effects of conductivity and thickness on shielding effectiveness (SE) across the X-band remain limited. This study presents a parametric finite element analysis of EMI shielding performance for graphite materials with electrical conductivities of 1, 10, 100, 1000, and 3000 S/m, representing structures ranging from highly porous to dense graphite. Thicknesses from 1 to 10 mm are simulated in a WR90 rectangular waveguide using finite element simulations. The Transition Boundary Condition (TBC) is employed to efficiently model conductive slabs without resolving the skin depth via volumetric meshing. Shielding effectiveness is evaluated from S-parameters and decomposed into total (SET), absorption (SEA), and reflection (SER) components. Results show that SET increases with both conductivity and thickness, and that shielding behavior is strongly dependent on conductivity. Low-conductivity graphite (σ = 1 S/m) exhibits absorption-dominated shielding, reaching an absorbed power fraction of 89% at 5 mm thickness. At σ = 10 S/m, the material enters a transitional regime where absorption and reflection contribute comparably. For highly conductive graphite (σ ≥ 100 S/m), reflection becomes dominant, with the reflected power fraction approaching 0.97 at σ = 3000 S/m. A conductivity-dependent saturation thickness is identified, beyond which additional thickness provides negligible shielding improvement. Dense graphite materials (σ ≥ 1000 S/m) reach saturation at approximately 1 mm thickness. Finally, a conductivity–thickness design heatmap is developed to guide the optimization of graphite-based EMI shielding materials. Full article

Increasing penetration of electric vehicles, heat pumps, and rooftop photovoltaics is creating thermal and voltage stress in low-voltage distribution grids. This work links the German Federal Government energy transition pathway (2025–2045) with state estimation performance requirements, evaluated at five milestone years from 2025 to 2045 on two SimBench reference networks across three equipment size levels (large, medium, small) and three VDE Forum Netztechnik/Netzbetrieb (VDE FNN) measurement constellations that differ in the availability of transformer- and feeder-level instrumentation. Within this work’s analysis, congestion is caused exclusively by transformer overloading and voltage-band violations. No individual line exceeds its thermal rating (maximum: 98.6%). Equipment size governs congestion onset for a given deployment trajectory: under large equipment, congestion remains absent through 2045, under medium equipment it emerges from 2035 (4 of 10 scenarios), and under small equipment from 2025 (9 of 10). Without transformer instrumentation, median voltage estimation errors reach 6–42% regardless of smart meter penetration. Adding a single transformer measurement reduces errors by an order of magnitude, achieving median errors of 0.5–1.4%. In urban networks, transformer-level instrumentation meets the VDE FNN voltage accuracy target (99th percentile voltage error below 2%) in all configurations. In rural networks under small equipment, the target is approached but not met. These findings motivate prioritizing transformer instrumentation as an effective first step for grid observability and supplementing the current consumption-driven metering rollout with risk-based deployment criteria linked to local congestion exposure. Full article

This work presents an integrated green chemistry and analytical approach for the removal of Brilliant Blue G (BBG) using the non-ionic poly(acrylate) resin Amberlite XAD7HP (XAD7HP), emphasizing structure–property–performance relationships relevant to the resin’s adsorption behavior. The UV–Vis method used for BBG quantification exhibited excellent linearity in the 10–30 mg/L range (R² = 0.9998). Batch adsorption experiments performed over 15–800 mg/L revealed a well-defined saturation profile, with the Langmuir model providing the best fit (R² = 0.9999) and indicating a monolayer capacity of 117 mg/g and highly favorable adsorption (R_L = 0.001). Kinetic evaluation showed rapid initial uptake followed by intraparticle diffusion, with the pseudo-second-order model offering the highest correlation (R² = 0.9811), while Weber–Morris analysis confirmed the contributions of both film and pore diffusion. FTIR-ATR analysis revealed only minor shifts (<10 cm⁻¹) in characteristic bands, confirming physisorption driven by hydrogen bonding, π–π interactions, dipole–dipole forces, and hydrophobic effects, without structural modification of the resin. SEM/EDX imaging demonstrated significant morphological changes after adsorption, including partial pore blockage and deposition of dye aggregates within the meso–macroporous network. XRD patterns confirmed the structural stability of the resin, while TG-DSC analysis highlighted its thermal robustness and suitability for reuse. Desorption studies showed that acidic–alcoholic systems (MeOH–HCl, EtOH–HCl) ensured the highest BBG recovery, supporting the regenerability of XAD7HP. Overall, the combined spectroscopic, kinetic, equilibrium, and morphological evidence demonstrates that XAD7HP is a stable, efficient, and reusable resin for BBG removal, offering a sustainable remediation pathway aligned with green analytical chemistry principles. Full article

This study evaluates the adsorption, structural characterization, and regeneration performance of acrylic resin Amberlite XAD7HP (X7) for the removal of metformin (MET), an emerging pharmaceutical contaminant. MET concentrations after adsorption were quantified using the linear UV–Vis method at 232 nm (R² = 0.9998). Adsorption kinetics followed the pseudo-second-order model (R² =0.9881), and equilibrium data fitted the Langmuir isotherm, confirming monolayer adsorption. Desorption experiments showed that acidic media were ineffective (<10%), whereas the mixed (1:1) MeOH–1M HCl system achieved 89.3% MET recovery, enabling efficient resin desorption. FTIR confirmed MET retention through attenuation of N–H stretching bands at 3360–3290 cm⁻¹, the shift of the C=N vibration from 1628 cm⁻¹ to 1605 cm⁻¹, and the appearance of a new band at 1542 cm⁻¹, indicating hydrogen bonding and dipole–dipole interactions with the resin. SEM micrographs revealed a clear transition from the X7, rough morphology to a smoother, partially occluded surface after adsorption, consistent with pore filling by MET. EDX analysis further confirmed MET uptake through the appearance of a distinct N signal and increased O content, serving as elemental markers of drug adsorption. TG/DSC demonstrated enhanced thermal stability and modified decomposition profiles for the resin loaded with MET, while XRD patterns confirmed the amorphous nature of X7 and the absence of crystalline MET deposits, indicating molecular-level dispersion. The integrated analytical, structural, kinetic, and desorption results highlight the potential of desorbed acrylic resin as a sustainable material for mitigating pharmaceutical pollution in aquatic environments. Full article

Land surface reflectance and temperatures data products are generated from data acquired with the Operational Land Imager and Thermal Infrared Sensor on the Landsat 8 and 9 satellites. The surface temperatures (ST) in the Level 2 surface products (L2SP) are produced using a single channel algorithm, which takes no consideration of the presence of cold thin cirrus clouds, that re-emit thermal radiation at lower temperatures. The errors in the ST data products over cirrus-contaminated areas can be larger than 10 K. Our recent analysis of Level 1 top of the atmosphere (TOA) data products (L1TP) and L2SP data products acquired over different geographic regions, show that it is possible to use the correlations between the Level 1 TOA reflectances of Band 9 (referred as the cirrus band) and the Level 2 ST data products to remove thin cirrus induced errors in the daytime Level 2 ST data products over water. The corrected daytime ST datasets have standard deviations reduced to about 1 K, similar to results with no cirrus present. End users of the Level 2 ST data products are encouraged to test this method to make cirrus corrections to the existing daytime ST data products, and to more broadly apply its results and further its applicability. Full article

Piezoelectric pressure sensors for dynamic monitoring face a trade-off between charge output and measurement range, and existing high-sensitivity designs are largely confined to narrow ranges. This study presents a composite layered piezoelectric pressure sensor in which a 316L stainless-steel diaphragm drives a centrally suspended PZT-5H wafer supported by a perforated alumina gasket, with the wafer thickness and cavity radius optimized under a 10 MPa full-scale stress constraint. Over 0–10 MPa, quasi-static calibration gave a highly repeatable quadratic pressure–charge relationship ($R^2=0.99995$) with a maximum residual below 1% FS. The sensitivity is pressure-dependent: the secant sensitivity increased monotonically from 3.16 pC/kPa at 1 MPa to 5.36 pC/kPa at 10 MPa, reflecting a stress-stiffening response rather than a measurement tolerance band. The output deviation remained within 3% from 25 °C to 150 °C. Shock-tube testing yielded a resonance of ∼50 kHz and a mutually consistent 10–90% leading-edge interval of 10.12 $\mathsf{\mu }$s. Combining high charge sensitivity over a wide 0–10 MPa range with a fast transient response and stable operation up to 150 °C, the proposed sensor is suited to dynamic pressure-pulsation monitoring in fluid-power and thermal and power-plant fluid systems. Full article

This study examines the adsorption and interaction mechanisms of Congo red (CR) immobilized onto maize stalk (MS) to form MS-CR material, used for the removal of Fe³⁺, Cr³⁺, and Mn²⁺ (Mⁿ⁺) from aqueous media. Initially, the MS was functionalized with CR, achieving adsorption capacities between 41.4 and 48.0 mg/g across pH 2–10, confirming the formation of hydrogen bonding and aromatic interactions, as indicated by the shift of the OH band from 3338.91 to 3335.54 cm⁻¹ and the appearance of characteristic azo–aromatic peaks (1601–1506 cm⁻¹) in the FTIR spectra. Stability tests showed that CR remains anchored to the lignocellulosic matrix even under 2 M HCl/NaOH. Subsequently, adsorption experiments revealed a strong pH dependence: at pH 10, removal efficiencies reached 93% for Mn²⁺, 89% for Fe³⁺, and 72% for Cr³⁺ at 2 mg/L, driven by surface deprotonation and enhanced electrostatic attraction. Increasing the initial metal concentration (1–10 mg/L) led to maximum adsorption capacities of 2.00 mg/g for Fe³⁺, 1.64 mg/g for Cr³⁺, and 1.46 mg/g for Mn²⁺. Desorption experiments identified 0.5 M HCl as the optimal regenerating agent, achieving 90–97% metal release. FTIR analysis of MS-CR–Mn²⁺ showed the disappearance of the 1243 cm⁻¹ carboxyl band and the emergence of a metal–oxygen vibration at 559.37 cm⁻¹, confirming adsorption via coordination to deprotonated carboxyl and phenolic groups. TG/DSC/DTG analysis demonstrated improved stability of MS-CR compared to native MS. SEM/EDX confirmed the presence of S, Na, and Mⁿ⁺. The combined spectroscopic, microscopic, and thermal evidence demonstrates that MS-CR operates as a robust, multifunctional adsorbent capable of Mⁿ⁺ retention, offering a sustainable solution for water treatment. Full article

Real-time, non-contact velocity measurement of hot-rolled bars is critical for metallurgical process control, but conventional laser Doppler velocimetry (LDV) systems often fail in these environments. The intense broadband thermal radiation from targets up to 1000 °C, coupled with severe surface depolarization, overwhelms weak scattered signals in high-speed (up to 40 m/s) rolling zones. To address this issue, we developed a fully integrated, thermal-radiation-resistant LDV sensing system. Hardware optimization was achieved by eliminating polarized-light transmission and adopting a parallel-beam design, which significantly enlarges the laser overlap area and increases detection depth. Furthermore, a 1550 nm laser (100 mW) was coaxially combined with a 10 nm narrow-band filter to isolate the thermal background and boost signal strength. A customized workflow utilizing continuous Fourier transform (CFT) spectral refinement and energy centroid estimation was implemented to precisely extract the true Doppler shift. Performance evaluations show the system achieves an excellent signal-to-noise ratio (SNR) of 29,532. Allan variance analysis confirms a stable detection sensitivity of 0.003 m/s (0.1 s integration time), a local short-to-medium-term optimal limit of 1.6 × 10⁻⁴ m/s, and a statistical accuracy of 0.005 m/s. Finally, the system was successfully deployed on an industrial rolling mill production line. It provided reliable velocity feedback for mill speed adjustment, achieving a near-zero-tension rolling process and fundamentally resolving workpiece dragging, squeezing, and steel pile-up. Full article

Land Surface Temperature (LST) is essential for climate monitoring, drought assessment, and urban heat analysis. Despite its importance, the Landsat-8 Collection 2 Level-2 (C2L2) LST product has not been rigorously validated using ground measurements—a critical gap this study addresses. We present the first comprehensive accuracy assessment using 382 coincident satellite–ground observations collected from seven Surface Radiation Budget Network (SURFRAD) stations distributed across diverse climatic regions of the United States during the period 2023–2025. The validation results indicate strong overall agreement between satellite-derived and ground-measured temperatures, yielding an RMSE of 4.20 °C, a coefficient of determination (R²) of 0.91, and a Pearson correlation coefficient (r) of 0.98. These statistics demonstrate the high reliability of the C2L2 LST product across a wide range of environmental conditions. Nevertheless, a systematic warm bias of 1.75 °C was observed, indicating a tendency toward temperature overestimation. Model performance exhibited pronounced seasonal variability. The highest accuracy was achieved during winter conditions (RMSE = 2.17 °C; r = 0.99), whereas performance declined considerably during summer months (RMSE = 5.84 °C; r = 0.91). Analysis of atmospheric water vapor content revealed significant associations with retrieval errors at high-elevation and arid locations, particularly at FPK (r = 0.78) and DRA (r = 0.75), based on 106 matched observations. These relationships provide important insight into the atmospheric factors contributing to seasonal variations in retrieval accuracy. Temperature-dependent analyses further demonstrated that retrieval uncertainty increases with surface temperature. Performance progressively deteriorated from cooler to warmer thermal regimes, with RMSE values increasing from approximately 2.05 °C for temperatures below 20 °C to 5.71 °C for temperatures exceeding 40 °C. Spatial evaluation also revealed substantial differences among stations. Relatively homogeneous, low-elevation sites exhibited superior performance (GWN: RMSE = 2.60 °C; SXF: RMSE = 2.55 °C), whereas stations located in mountainous or topographically complex environments showed reduced accuracy (TBL: RMSE = 5.14 °C; FPK: RMSE = 5.62 °C). These outcomes emphasize the influence of terrain complexity and atmospheric heterogeneity on LST retrieval performance. Overall, this study establishes the first comprehensive benchmark for evaluating the reliability of Landsat-8 C2L2 LST products. The results provide valuable guidance for their application in climate research, precision agriculture, hydrological modeling, and environmental monitoring. Furthermore, the findings identify specific environmental conditions requiring enhanced validation efforts and suggest opportunities for future algorithm refinement through improved atmospheric correction procedures and more accurate surface emissivity characterization. Full article

Predicting forest fire occurrence is essential for proactive disaster preparedness and environmental protection. We introduce a machine learning-based system that forecasts next-day fire probability at high spatial resolution using satellite-derived, multi-modal geospatial data. In contrast to existing reactive systems that rely on thermal anomaly detection (e.g., MODIS or VIIRS-SNPP), our approach is fully predictive, generating pixel-wise fire risk maps a day in advance. Our study focuses on Uttarakhand, India, which is an ecologically sensitive region that experiences frequent and severe forest fires. We curated a domain-specific geospatial dataset spanning 1 April to 29 May 2016. It includes daily 30 m GeoTIFF images with 10 bands comprising weather (e.g., temperature, wind, precipitation), topography (slope, aspect), fuel map, and fire mask. We constructed this dataset from diverse sources and aligned all bands spatially and temporally. To demonstrate the usefulness of this dataset, we implement a deep convolutional neural network (CNN) using the ResUNet-A architecture, chosen for its robust performance in the semantic segmentation of high-resolution remote sensing data. Our model is trained from scratch to produce high-resolution fire probability maps and classify fire/no-fire pixels. Our solution helps with planning and decision-making for early intervention, especially in areas with high risk. It supports the UN’s SDG 13 (Climate Action) and SDG 15 (Life on Land) by enhancing resilience and conserving ecosystems. The presented dataset and methodology can serve as a benchmark for future research on wildfire risk prediction using Earth observation data. Full article

Viscose-derived carbon fibers (VDCFs) are lightweight and flexible textile materials with strong potential for electromagnetic interference (EMI) shielding; however, their performance is governed by surface chemistry. This study aims to tailor the functional properties of VDCFs via process-driven sulfurization. The fibers were treated with sulfur vapor at 400–800 °C under argon, followed by rapid quenching, enabling controlled sulfur incorporation (0.5–12 mmol g⁻¹). Structural and chemical analyses (XRD, SEM–EDS, ATR–FTIR, and TPD–MS) revealed temperature-dependent sulfur incorporation and evolution of sulfur-containing surface functionalities. Sulfurization at 400–500 °C favored the formation of thermally labile sulfur species, tentatively assigned to mercapto-, sulfide-, and polysulfide-type groups, whereas higher treatment temperatures promoted more thermally stable sulfur-containing functionalities associated with the carbon framework. Two desorption regimes (120–250 °C and 250–500 °C) indicate the coexistence of weakly and strongly bound sulfur species. Importantly, sulfurization preserved fibrous morphology while increasing surface roughness and defect density, enhancing interfacial activity. The treatment temperature was identified as the key factor controlling sulfur loading and distribution, with sulfur content continuing to decrease above 600 °C, albeit at a reduced rate. Electromagnetic characterization in the X-band (8–12 GHz) showed a transition toward reflection-dominated EMI shielding, with reflectivity increasing from 87% for pristine fibers to 94–95% for sulfurized samples at 10 GHz, accompanied by corresponding decreases in transmission and absorption. These results demonstrate a clear processing–structure–property relationship and highlight sulfur-functionalized VDCFs as efficient textile components for EMI shielding. Full article

Pharmaceutical residues continue to increasingly contaminate water systems at a global level, and conventional wastewater treatment plants are unable to completely remove these emergent compounds. This study investigates the benzocaine adsorption from aqueous solutions onto Amberlite XAD-7 (X7) resin, with emphasis on quantitative performance metrics and mechanistic understanding. Adsorption occurred rapidly, reaching equilibrium within 60 min, with a maximum adsorption capacity (Q_e) of 140 mg/g and a Langmuir monolayer capacity of 147 mg/g. Experimental parameters strongly influenced X7 performance: resin dosage (0.01–0.05 g) and agitation speed (25–200 rpm) enhanced removal efficiency from 10% to 99.9%, while pH variation (5–9) had a negligible effect, confirming a predominantly hydrophobic, non-ionic adsorption mechanism. Equilibrium data are best described by the Langmuir model (R² = 0.9920, b = 5.2 L/mg, RL = 0.0003), indicating highly favorable monolayer adsorption, while kinetic behavior is described by the pseudo-second-order (PSO) model. FTIR-ATR analysis confirms benzocaine retention through characteristic shifts in aromatic, amine, and ester bands. TG/DSC measurements prove the thermal stability of X7 and the incorporation of benzocaine within the polymeric matrix. Desorption efficiencies ranged from 40% (NaOH) to 97% (HCl-ethanol mixture), demonstrating that X7 was regenerated under the tested conditions with a single cycle. Overall, X7 exhibits high capacity, robustness, and recyclability, highlighting its strong potential for efficient benzocaine removal from contaminated wastewater. Full article

The Janus monolayers have recently attracted substantial interest due to their unique asymmetric structures and intriguing physical properties. In this work, we explore the thermoelectric properties of the Janus monolayer ZrBrI, using first-principles calculations and Boltzmann transport theory. We demonstrate that the system maintains good dynamic and thermal stability, as evidenced by the absence of imaginary phonon modes and small lattice fluctuation at a higher temperature of 600 K. The hybrid functional calculations reveal that the monolayer exhibits a relatively small indirect gap of 1.22 eV, and the energy bands near the conduction band minimum exhibit double degeneracy with weak dispersions, which is very beneficial for enhancing the n-type power factor. Meanwhile, a relatively lower lattice thermal conductivity is found due to strong lattice anharmonicity caused by the antibonding state and the symmetry breaking of the structure. Collectively, a larger ZT value of 3.9 at 600 K can be realized for the n-type Janus monolayer ZrBrI at an optimal concentration of $1.89\times {10}^{13} {\mathrm{cm}}^{-2}$, highlighting its promising thermoelectric application in the intermediate temperature region. Full article