Search Results (3,083)

Accurate and robust battery health prognostics are critical for reliable battery management in electronic devices and electric vehicles. Previous studies have demonstrated that combining electrochemical impedance spectroscopy (EIS) with machine learning enables accurate health-state forecasting in LiCoO₂ coin cells. However, the applicability of this EIS-AI paradigm across diverse chemistries and industrial-grade battery formats remains unvalidated, limiting its practical deployment in energy storage systems. Here, we develop an EIS–AI battery prognostic framework and validate its performance on LiNi_1/3Mn_1/3Co_1/3O₂ (NMC111) cylindrical cells and LiNi_0.8Mn_0.1Co_0.1O₂ (NMC811) pouch cells. A hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) architecture is developed to estimate state of health (SoH) and predict remaining useful life (RUL) from EIS spectra. Trained on an in-house dataset comprising over 13,000 impedance spectra from 22 cells (8 NMC111 and 14 NMC811), the model achieves robust performance, with average coefficients of determination (R²) exceeding 0.92 for SoH estimation and 0.90 for RUL prediction across various batteries and cycling protocols. Salient feature analysis further reveals chemistry- and protocol-dependent frequency regimes associated with degradation. These results demonstrate that impedance spectra constitute physically informative descriptors for data-driven battery prognostics and provide a scalable and interpretable pathway for deploying EIS-AI frameworks in real-world battery management systems (BMSs). Full article

Polycrystalline La_2−xBi_xNiMnO₆ (x = 0.2, 0.5, 1.0) samples were synthesized via a high-temperature and high-pressure method, with their structural, magnetic, and electrical properties systematically characterized. X-ray diffraction (XRD) confirms a monoclinic double perovskite structure (space group P2₁/n) for all samples, while Bi³⁺ induces a lattice volume expansion trend inferred from XRD peak shifts due to its larger ionic radius than La³⁺. Magnetically, all exhibit ferromagnetism and soft magnetic features, with magnetization decreasing as Bi content increases. The x = 0.2 and 0.5 samples show two distinct Curie temperatures, both decreasing with Bi substitution, whereas the higher Curie temperature vanishes in the x = 1.0 sample, likely due to Bi-induced structural changes. Electrically, all display semiconducting behavior (resistivity: x = 0.5 > x = 0.2 > x = 1.0) and negative magnetoresistance (MR) at 200 K, peaking at 12% (x = 0.5) and 7.5% (x = 1.0). For the x = 1.0 sample, negative magnetoresistance strengthens with decreasing temperature (130–200 K), with magnetoresistance-field (MR-H) curves showing herringbone and arc shapes. Full article

Bismuth oxychloride (BiOCl) has garnered significant research interest owing to its non-toxicity, affordability, and distinct layered structure. Although BiOCl possesses promising photocatalytic potential, its large band gap and rapid photocarrier recombination restrict its practical use. In this work, a natural tourmaline mineral was effectively integrated with BiOCl to form a composite (TBO). Comprehensive characterization and photocatalytic assessments revealed that the intrinsic electric field of tourmaline notably strengthened both the adsorption capacity and the light-driven catalytic efficiency of BiOCl. Under visible-light irradiation, ofloxacin (OFX, 10 ppm) was eliminated by approximately 98% within 60 min. The apparent reaction rate constant (k) of TBO was 0.0407 min⁻¹, which was approximately 184.8 and 2.26 times those of tourmaline alone and pristine BiOCl, respectively. Furthermore, both the visible-light absorption and the separation efficiency of photogenerated electron–hole pairs were significantly enhanced. After evaluating its behavior under various simulated natural environmental conditions, TBO displayed strong potential for practical application. Reactive species trapping and analysis identified singlet oxygen (¹O₂) and superoxide radicals (·O₂⁻) as the primary active species in photocatalysis. Moreover, the degradation route of ofloxacin and the toxicity of its intermediates were systematically examined. These findings offer meaningful guidance for improving photocatalytic materials by utilizing naturally occurring minerals. Full article

Air pollution forecasting is particularly challenging in basins with frequent winter seasons and temperature inversions. In this study, we developed and rigorously evaluated deep learning models to forecast PM₁₀ and the Air Quality Index (AQI) in Igdır, Türkiye, using a five-year, hourly dataset (2020–2024) from the Igdır/Central station (PM₁₀, NO₂, O₃, SO₂; meteorology: pressure, temperature, wind speed, relative humidity, precipitation, cloud cover). Using linear interpolation and Z-score normalization, sine/cosine features (hour, month) were used to encode temporal periodicity, and a 72-h lookback → 24-h look-ahead design was employed. LSTM, GRU, BiLSTM, and CNN-LSTM models were compared under a three-stage ablation (meteorology only; +cyclic encoders; +lagged targets), and their hyperparameters were tuned via Bayesian optimization. The deep learning results were further contextualized against a Multiple Linear Regression (MLR) baseline serving as a snapshot persistence model to evaluate the specific advantage of LSTM’s temporal memory in short-horizon forecasting. Multi-output forecasting is central to the proposed design, featuring a multi-task learning (MTL) framework based on a single shared temporal encoder with two task-specific regression heads that simultaneously predict PM₁₀ and AQI. Compared with separate single-task models, the multi-output setup exploits cross-target covariance (AQI’s dependence on pollutant loads under meteorology), improves data efficiency and generalization through shared representations, and promotes coherent, horizon-stable forecasts across targets, which is particularly valuable when winter stagnation regimes couple PM₁₀ and AQI dynamics. Moreover, this study introduces a structured ablation design to explicitly evaluate the added value of multi-output forecasting under inversion-dominated basin conditions. The results show stepwise gains from cyclic encoders and, most strongly, from lagged target histories. Under the optimized 24-h setting, LSTM performs best (R²_{PM₁₀} = 0.7989, RMSE = 48.74 µg/m³; R²_{AQI} = 0.6626, RMSE = 37.81), marginally surpassing GRU and clearly outperforming BiLSTM and CNN-LSTM. Horizon sensitivity confirms the benefit of nowcasting: when retrained for shorter horizons, LSTM attains R² = 0.9991 for PM₁₀ (MAE = 2.44; RMSE = 3.30 µg/m³) and 0.9535 for AQI (MAE = 4.87; RMSE = 14.03) at 1 h, and R² = 0.9792 (PM₁₀; MAE = 9.70; RMSE = 15.67) and 0.8849 (AQI; MAE = 11.19; RMSE = 22.08) at 6 h. Residual diagnostics reveal heteroskedastic, regime-dependent errors peaking near 0 °C and low winds, as well as a conservative bias that underpredicts extremes. Collectively, the findings show that multi-output, temporally aware deep models enable accurate operational forecasting in Igdır. The proposed framework provides real-time air quality alerts and daily planning, providing decision support for sustainable air quality management, public health protection, and evidence-based urban policy and is transferable to similar continental basin environments. Full article

Bi-doped rare-earth iron garnet (Bi:RIG) single crystals are the core of optical isolators, and demand for them is surging due to the development of artificial intelligence (AI) technology. In this work, bismuth-doped terbium iron garnet (Bi:TbIG) single crystals with a composition of Bi_0.86Tb_2.14Fe₅O₁₂ and a size of 37 mm were successfully grown by the top-seeded solution growth (TSSG) method using a lead-containing flux system. These crystals exhibited a regular rhombic dodecahedron morphology enclosed by the {110} plane, and a growth rate of 0.018 mm/h perpendicular to the {110} planes. Systematic characterizations revealed that the crystals exhibited good compositional homogeneity, with no obvious Fe, Tb and Bi segregation from center to edge. Rocking curve tests presented a full width at half maximum of 172 arcsec. X-ray photoelectron spectroscopy (XPS) results demonstrated that Fe exists exclusively in the +3 valence state without detectable Fe²⁺, whereas Tb is present in the +4 valence state. In addition, O was lattice O²⁻, without obvious defects. Magneto-optical tests indicated that the uncoated TSSG-grown Bi:TbIG crystals had 71% transmittance in the 1200~1600 nm waveband, and a Faraday rotation coefficient of 0.132°/μm at 1310 nm. The 11 × 11 mm samples exhibited an extinction ratio stably above 40 dB. The 349 μm thick samples meet the application requirements of miniaturized optical isolators. This study verifies the feasibility of TSSG for growing Bi:TbIG single crystals, offering a new technical route for Bi:TbIG growth with potential value for practical application. Full article

To deal with the high standards, strong restrictions, and high repeatability that are inside State Grid small-scale infrastructure projects, this research puts forward a norm-driven generative design method, which conquers the low efficiency, compliance dangers, and semantic breakage that are usual in manual modeling. Taking standards such as Q/GDW 11382.3-2015 as the knowledge origin, we construct an ALBERT-BiLSTM-CRF semantic parsing model and change natural-language clauses into executable design restrictions via normative text pre-processing, BIO sequence marking, and rule triplet mapping. Therefore, model training and assessment produce Accuracy, Precision, Recall, and F1 of 98.05%, 95.49%, 95.88%, and 95.59% separately, with 100% precision for logical comparison and conjunction labels; thus, this provides a steady semantic base for the rule base. At the component level, a three-part coding plan and unit module collection are built based on OmniClass and GB/T 51269, which makes semantic consistency and traceability between components and space functions possible. At the system level, a continuous work process is carried out through the Revit API, which covers scheme making, automatic arrangement, and deliverable output. Hence, validation on a real case in a digital operation center for the power system shows that the design time for the third-floor administrative office area was cut from about 20 h to around 4 h, and the first-time solution met all code restrictions, which improves efficiency and compliance in a significant way. The results point out that norm-driven generative design can supply deployable automation and high-quality outputs for small-scale power infrastructure, which provides a sustainable database for digital twins and smart O&M. Full article

Background and Objectives: One of the two primary classes of drugs administered in ICUs for pharmacological sedation is benzodiazepines. Among these, anesthesiologists consider midazolam the most commonly used and clinically significant agent. Materials and Methods: A prospective, single-center investigation involving 25 patients was carried out in the ICU. The study population consisted of patients undergoing mechanical ventilation with an FiO₂ exceeding 60%, as well as ventilated individuals requiring additional support such as ECMO, NO, or ECCOR over 24 h before the study. Participants under 18 years of age or those not receiving continuous midazolam infusion were excluded. Measurements obtained from RASS and BIS were then compared with serum midazolam concentrations. On each day, when blood samples for midazolam measurements were taken, additional laboratory tests assessing renal and hepatic function were also carried out. Results: A negative correlation was shown between RASS and midazolam dosage (r = −0.44, p < 0.001), midazolam concentration (r = −0.33, p < 0.001), and α-OH-midazolam concentration (r = −0.24, p = 0.008). Similarly, a negative correlation was shown between BIS and midazolam concentration (r = −0.3, p = 0.016), as well as α-OH-midazolam (r = −0.3, p = 0.016). We observed that deceased patients received higher doses of midazolam to maintain the minimum level of required sedation compared to the others (135.5 ± 75.1 mg vs. 39.6 ± 59.2 mg; p = 0.002), indicating that these patients had higher concentrations of both midazolam and α-OH-midazolam (148.6 ± 83.5 µg/L vs. 27.2 ± 36.1 µg/L; p < 0.001, and 18 ± 15.9 vs. 5.3 ± 6.1 µg/L; p < 0.001). Conclusions: The results show that routine monitoring of midazolam does not provide additional clinical value. However, further studies are needed in high-risk groups. Despite the high mortality rate in the ICU for patients with severe respiratory failure, the six-month survival rate for discharged patients was high, exceeding 80%. Full article

Apple detection is a core perception task for harvesting robots operating in complex orchard environments. Targets are frequently affected by branch–foliage occlusion, alternating front/side/back lighting, and strong local illumination fluctuations, which blur object boundaries against background textures and substantially increase detection difficulty. To improve target perception under these conditions, we propose an improved detector, YOLOv11-CBMES. First, based on YOLOv11, we replace the original neck with a weighted BiFPN to enhance cross-scale feature fusion under occlusion. Second, we introduce a Contrast-Driven Feature Aggregation (CDFA) module at the P5 stage, using Haar wavelet decomposition to decouple low-frequency illumination components from high-frequency structural components. Third, we reconstruct spatial feature learning and the upsampling pathway using CSP-based multi-scale blocks and efficient upsampling blocks, and embed a zero-parameter Shift-Context strategy to strengthen local neighbourhood interaction. Finally, we formulate apple detection as a three-class occlusion classification task (No Occlusion, Soft Occlusion, and Hard Occlusion) to support occlusion-aware target recognition. On the apple occlusion dataset, YOLOv11-CBMES achieves ${\mathrm{m}\mathrm{A}\mathrm{P}}_{\mathrm{N}\mathrm{O}}$ = 83.50%, ${\mathrm{m}\mathrm{A}\mathrm{P}}_{\mathrm{S}\mathrm{O}}$ = 67.36%, and ${\mathrm{m}\mathrm{A}\mathrm{P}}_{\mathrm{H}\mathrm{O}}$ = 51.90% at IoU = 0.5. Compared with YOLOv11n under the same training protocol, the gains are +2.16 pp (NO), +3.68 pp (SO), and +5.31 pp (HO), with the largest improvement observed in Hard Occlusion (HO). The results indicate that introducing frequency-domain structural processing into the detection framework improves apple occlusion classification and object detection performance, and provides a theoretical basis for designing perception modules for end-effector operations in apple harvesting robots. Full article

In response to global climate change, virtual power plants (VPPs) have emerged as critical entities for integrating distributed energy resources and enabling demand response. However, the design of multi-energy collaborative pricing mechanisms for VPPs remains a significant challenge, particularly under carbon trading regulation. This paper addresses this gap by proposing a bi-level optimization model that captures the real-time interactions between users and energy suppliers. The model is designed to simultaneously maximize user utility and minimize supplier costs, explicitly accounting for energy costs, equipment operation and maintenance (O&M) costs, carbon emission costs, and power generation structure constraints. A particle swarm optimization (PSO) algorithm is employed to solve the formulated problem. The results of a case study demonstrate that the proposed mechanism effectively guides users toward peak shaving and valley filling, achieving a real-time balance between supply and demand. Furthermore, the simulation results indicate that the model significantly enhances power system operational efficiency and economic benefits while reducing carbon emissions. This work offers a practical approach for improving renewable energy integration and overall system performance within a carbon-constrained environment. Full article

Lithium metal batteries (LMBs) have been widely studied due to their high energy density; however, the practical implementation of LMBs is limited by issues of uncontrolled dendrite growth, continuous electrolyte decomposition, and poor Coulombic efficiency (CE). Highly concentrated electrolytes (HCEs) have emerged as a promising approach to addressing the above issues. In this work, we propose a new HCE system based on a single carbonate solvent of 2,2,2-trifluoroethyl methyl carbonate (FEMC) with a high concentration of lithium bis(fluorosulfonyl)imide (LiFSI). The resulting electrolytes exhibit enhanced anodic stability and improved compatibility with lithium metal anodes and high-voltage cathodes. The optimized 4 M LiFSI–FEMC HCE achieved the highest CE for Li plating/stripping in Li/Cu cell and lowest overpotential in Li/Li symmetric cells, outperforming both low-concentration FEMC and ethyl methyl carbonate (EMC)-based electrolytes. In full-cell configurations with LiNi_0.8Co_0.1Mn_0.1O₂ (NCM811) cathodes, the HCE demonstrates stable cycling with minimal capacity fade over 250 cycles. Importantly, the HCE enables stable operation of 4.6 V high-voltage NCM811/Li cells for more than 120 cycles with a high-capacity retention of 88.61%. Post-mortem analysis revealed a more uniform and compact solid electrolyte interphase and a thinner cathode electrolyte interphase, contributing to the enhanced cycling performance. Full article

This paper presents an analysis of the physicochemical and biological properties of the developed composite zinc phosphate cement modified with bismuth oxide and phosphorus slag additives. The powder phase was synthesized by sintering a frit with an optimal composition (ZnO, MgO, SiO₂, Bi₂O₃) using phosphorus slag as the active component. The study included an assessment of the microstructure, chemical resistance in aggressive environments (5% NaCl solution, 10% lactic acid, carbonated water), solubility in artificial saliva, and cytotoxicity in human fibroblasts. The addition of phosphorus slag was found to promote the formation of low-melting eutectics, which reduces the sintering temperature by 100 °C and increases the material’s whiteness to 97.8%. X-ray diffraction analysis confirmed the presence of zincite, quartz, and periclase phases, forming a dense microstructure without pronounced pores or cracks. The experimental cement demonstrated high acid resistance: the maximum weight loss in lactic acid was 8%, while the leaching of toxic elements (Pb, As, Cr, etc.) remained extremely low (10–67 ppm), confirming the material’s environmental safety. Testing of the composite zinc phosphate cement in artificial saliva revealed minimal weight loss compared to similar products. Biological testing showed that the cement’s cytotoxicity is dose-dependent; at a 0.3 g dose and a 1:4 dilution, the material loses its toxic properties and becomes safe for living tissue. The developed zinc phosphate composite cement composition offers improved aesthetic and mechanical properties, high chemical stability, and biocompatibility at working concentrations, making it promising for use in clinical dentistry. Full article

Defect engineering and metal–support coupling provide an effective route to tune interfacial charge dynamics for selective photocatalytic CO₂ reduction. Here, Ti-vacancy-rich Bi₄Ti₃O₁₂ (BTvO) nanosheets were prepared and decorated with Au nanoparticles (Au NPs) to build Au-BTvO junctions that favor multi-electron/proton transfer toward deep hydrogenation. The optimized 3%Au-BTvO achieved high hydrocarbon productivity under visible light (λ > 420 nm), delivering CH₄ and C₂H₆ formation rates of 92.66 and 17.96 μmol g⁻¹ h⁻¹, respectively, with stable performance over 25 h. Spectroscopic analyses reveal higher CO₂ uptake and more effective surface activation, increased water adsorption with a more favorable interfacial hydration environment, and time-dependent formation of key C₁ and C₂ intermediates. In situ light-irradiation XPS, PL mapping, and KPFM collectively demonstrate directional electron transfer from Bi₄Ti₃O₁₂ to Au and amplified surface band bending, enabling efficient charge separation and accelerated surface reduction. This work highlights defect–metal synergy as a general strategy to boost activity, selectivity, and durability in visible-light CO₂-to-methane conversion. Full article

The activation of molecular oxygen driven by solar energy presents a cost-effective and environmentally friendly approach in the area of environmental purification. Carbon quantum dots and semiconductor nanocomposite photocatalysts serve as an effective strategy for enhancing the separation and transport of photogenerated carriers, thereby boosting the activation of molecular oxygen. In this study, we prepared 0D tea biomass quantum dots (T-BCDs) coupled with 2D PbBiO₂Br nanosheets, which demonstrate enhanced molecular oxygen activation under visible light irradiation and were synthesized using a solvothermal method. Transmission electron microscopy (TEM) analysis reveals that T-BCDs, with diameters of approximately 5 nm, are uniformly distributed on the surface of PbBiO₂Br. Notably, experimental results indicate a strong covalent interaction between PbBiO₂Br and T-BCDs, which enhances the absorbance of visible light, facilitates the transfer and separation of interfacial photogenerated carriers, and promotes the conversion of molecular oxygen into superoxide radicals. The degradation rate constant of ciprofloxacin achieved with 5 mL T-BCDs/PbBiO₂Br is 3.3 times greater than that obtained with pure PbBiO₂Br. This research offers a promising strategy for the development of efficient 0D/2D photocatalysts aimed at sustainable environmental remediation. Full article

The Yechangping super-large porphyry–skarn deposit is a key component of the East Qinling molybdenum metallogenic belt, central China. Magnetite is widely developed across all mineralization stages of this deposit, yet its systematic geochemical evolution and prospecting significance remain poorly constrained. This study presents in situ major- and trace-element analyses of magnetite via electron probe microanalysis (EPMA), laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), and elemental mapping, to unravel the ore-forming hydrothermal evolution and establish reliable prospecting indicators. Four magnetite generations are identified based on petrography and paragenetic relationships: late skarn stage (Mt1), oxide stage (Mt2 and Mt3), and polymetallic sulfide stage (Mt4). Magnetite has total iron contents (TFeO, total Fe calculated as FeO) of 82.72–95.46 wt.% (values above the 93 wt.% stoichiometric limit of pure magnetite stem from minor oxidation), with dominant isovalent Fe³⁺ and Al³⁺ lattice substitution supported by a significant negative Fe–Al correlation. Systematic stage-dependent geochemical variations are observed: Mt1 has the highest Ti (mostly >1500 ppm), V and Cr, while Mt2–Mt4 show progressive Ti depletion (mostly <100 ppm), recording continuous cooling of the hydro-thermal system. V and Cr contents decrease markedly from Mt1 to Mt3, with secondary enrichment in Mt4; Mo concentrations peak in Mt2 (average 5.06 ppm), coupled with elevated chalcophile metalloid Te, As, Pb and Bi. Elemental mapping results show that K occurs as discrete hotspots, which may be mainly derived from feldspar microinclusions, rather than lattice substitution in magnetite. These geochemical fingerprints record a transition from high-temperature magmatic–hydrothermal fluids to late contact-metasomatic fluids, with evolving fluid–rock interaction and oxygen fugacity. Our results demonstrate that magnetite chemistry is a reliable tool for discriminating mineralization stages and vectoring prospecting targets in porphyry–skarn Mo–W systems. Full article

A library of twelve heteroditopic bis-urea and bis-thiourea receptors supported on Merrifield and Wang resins was prepared by solid-phase synthesis. The receptors incorporate dual hydrogen-bond-donor units for anion binding and a polyether spacer that simultaneously functions as a cation-binding site, enabling ion-pair recognition at the solid–liquid interface. Molecular recognition studies were performed using several inorganic and tetraalkylammonium salts, and fluorescence changes were monitored by microplate measurements in DMSO and DMSO/H₂O (95:5, v/v). Univariate and factorial statistical analyses revealed statistically significant fluorescence changes and identified the structural variables governing guest recognition in each medium. Under the conditions examined, several systems exhibited reproducible ion-pair-induced fluorescence responses, highlighting the influence of receptor type and spacer architecture. These findings provide a basis for the rational optimization of supported receptors for sensing and extraction applications. Full article