Search Results (534)

Object detection in complex industrial environments is prone to being affected by insufficient dynamic weighting of local and global features, as well as illumination variations and impurities. Moreover, existing models suffer from excessive model complexity, which directly impairs computational efficiency. To more accurately distinguish Chinese herbal materials with diverse morphologies, this paper proposes the MobileAttn module. Drawing on the idea of token representation in the Transformer architecture, this module extracts contextual information through global feature compression, fuses it with tokens to generate a spatial attention map, and realizes dynamic recalibration of convolutional features. This process enhances the feature weights of key semantic regions, suppresses redundant background information, and improves feature discriminability. To address illumination interference, brightness-aware weights are combined with dual-path (channel and spatial) attention for global control, dynamically reducing the impact of illumination; this component is named LightAttn. When Chinese herbal materials contain common industrial unknown impurities (e.g., small stones and weeds), an impurity detection auxiliary module, a post-processing step independent of the main detection network, is proposed. This module refines Non-Maximum Suppression (NMS) logic to distinguish target Chinese herbal materials from interfering impurities. Subsequently, it accurately locates and marks impurities on the conveyor belt, thereby achieving effective unknown impurity detection. Experimental results demonstrate that, compared with the original YOLOv11 on the Chinese herbal materials detection task, the optimized model achieves a 1.7% improvement in the overall mean Average Precision (mAP@0.5:0.95). On a per-class basis, gains are particularly pronounced for certain challenging high-aspect-ratio Chinese herbal materials. Prunella vulgaris and orange peel achieve respective AP improvements of 5.8% and 4.1%. Meanwhile, the model parameter count is reduced by 23.1% and the computational complexity by 20.3%. The F1-Score of the impurity detection results is 86.38%, verifying the effectiveness of the impurity detection auxiliary module. Full article

Lithium carbonate is a cornerstone therapy for bipolar disorder, typically administered long-term, which necessitates strict control of elemental impurities beyond the quantification of the active ingredient. While previous studies focused on lithium concentration and dosing accuracy, this study characterized the unique inorganic signatures and evaluated the toxicological implications of reference, similar, and generic lithium carbonate formulations marketed in Brazil. Seven commercial brands were analyzed by inductively coupled plasma optical emission spectrometry (ICP OES). Elemental concentrations (mg/kg) ranged as follows: As (0.50–0.62), Pb (0.39–0.57), Se (0.80–1.01), Cr (detected in one similar formulation at 0.18), Fe (<LOD–0.86), Mg (8.10–14.65), K (1.18–4.2), Mn (0.072–0.40), and P (24.3–74.4), while Cd, Cu, and Zn were below detection limits. Statistical analysis (p < 0.05) demonstrated significant inter-manufacturer differences, indicating that pharmaceutical equivalence does not imply inorganic identity. Despite this variability, all formulations complied with ICH Q3D (R2), USP <232>, and Brazilian Pharmacopoeia limits. Under maintenance doses of 600–1200 mg/day, daily exposure remained well below Permitted Daily Exposure thresholds; the cumulative Hazard Index was <0.02, and Incremental Lifetime Cancer Risk (5.46 to 6.80 × 10⁻⁶) was within safe levels. These findings confirm that while distinct elemental signatures exist, the medications are toxicologically safe for chronic therapy. Full article

The rapid growth of photovoltaic panels installations has led to a dramatic increase in the end-of-life (EoL) panels, creating an urgent need for efficient recycling strategies. In the present study, a pretreatment system consisting of hydrochloric acid was developed to remove impurity metals such as aluminum and iron from EoL PV panel powder prior to the precious metals leaching step. Response surface methodology (RSM) based on a central composite design (CCD) was employed to optimize the effects of main operational parameters, i.e., HCl concentration, leaching time, and solid-to-liquid (S/L) ratio on the dissolution of Al, Fe, Pb, Sn, and Cu. Thermodynamic analysis with the help of HSC Chemistry^® 10 software, confirmed the feasibility of dissolution of the Al, Fe, Pb, Sn, and Cu in chloride media. Experimental results demonstrated that the dissolution rate of Al and Fe under optimal conditions were 86.05 and 91.77 percent, respectively. In all of the tests, copper dissolution remained negligible (<4%), and no silver was detected which confirms the selectivity of the pretreatment. The optimized conditions (1.5 M HCl, 198 min, 20% S/L) enabled effective impurity removal while preserving silver in the solid residue. This study highlights the importance of selective pretreatment in enhancing downstream silver recovery and provides a practical approach for the hydrometallurgical recycling of end-of-life PV waste. Full article

Residual volatile organic compounds (VOCs) and non-intentionally added substances (NIASs) limit the reuse of post-consumer recycled high-density polyethylene (rHDPE) in high-value applications because they generate persistent odors and may compromise product quality and regulatory acceptance. This work comparatively assesses five deodorization and purification routes for rHDPE: agitation washing, ultrasound-assisted washing, reflux heating, Soxhlet extraction, and dissolution/precipitation, by combining VOC removal performance, material characterization, and techno-economic evaluation. Ultrasound-assisted washing with SDS achieved ~96% total VOC removal, while reflux heating resulted in near-complete removal (~98%), approaching the analytical detection limit. Soxhlet extraction with ethanol reached 94% after 1 h, and the dissolution/precipitation method provided near-complete purification and removed additional impurities, but at the expense of substantially higher process complexity and cost. Mechanical and physicochemical characterization indicated that the evaluated treatments did not appreciably compromise the measured properties of the recycled polymer. In addition, equilibrium screening with representative analytes in ethanol provided qualitative support for the solvent–polymer interaction discussion. A plant-scale techno-economic assessment identified ultrasound-assisted SDS washing as the most attractive option, offering the best balance between deodorization efficiency, process simplicity, and cost. Overall, the results provide a practical basis for selecting scalable decontamination strategies to upgrade rHDPE quality and expand its use in circular plastic applications. Full article

Accurate measurement and control of impurities are critical for ensuring the quality and therapeutic performance of solid-state pharmaceutical formulations. This study introduces a rapid, minimal sample preparation analytical approach for quantifying low-level dalmelitinib impurities in dalmelitinib mesylate, employing near-infrared (NIR) spectroscopy combined with partial least squares regression (PLSR). To mimic actual manufacturing conditions, a mixture system was designed comprising dalmelitinib mesylate, dalmelitinib impurity, and formulation excipients. Various spectral preprocessing strategies were systematically evaluated, including Savitzky–Golay first derivative (SG1st), Savitzky–Golay second derivative (SG2nd), multiplicative scatter correction (MSC), standard normal variate (SNV), wavelet denoising, wavelet compression, and their combinations. The optimal model was obtained using SG1st combined with wavelet denoising. The resulting PLSR model (7 latent variables) showed good predictive performance, with an R² of 0.99569 and an RMSECV of 0.00315. The limit of detection (LOD) and limit of quantification (LOQ) were 0.234% and 0.708%, respectively, demonstrating applicability for monitoring low-level impurities in pharmaceutical formulations. Method validation demonstrated satisfactory precision (RSD < 3%), accuracy (100.77–102.01%), and stability over 24 h (RSD ≤ 4.75%). Compared with conventional solid-state analytical techniques, the proposed NIR–PLSR framework enables rapid, non-destructive analysis with minimal sample preparation. The combination of derivative preprocessing and wavelet denoising improved extraction of impurity-related spectral information in complex pharmaceutical systems, highlighting the potential of this approach for process analytical technology (PAT) and pharmaceutical quality monitoring. Full article

This study experimentally evaluates hydrogen recovery from synthetic syngas and water–gas shift (WGS) syngas using a laboratory-scale pressure swing adsorption (PSA) unit equipped with layered activated carbon/zeolite 5A beds. Breakthrough tests were first performed to determine adsorption-time limits and identify the critical impurity controlling product quality. Continuous PSA experiments were then carried out using two cycle configurations: a two-bed Berlin-type cycle and a four-bed Linde-type cycle. CO was the first impurity breakthrough experimentally detected and it therefore defined the practical adsorption-time cut-off, whereas CO₂ exhibited the strongest retention, especially in beds with an increased activated-carbon fraction. The results showed a clear trade-off between purity and recovery. The four-bed Linde-type cycle provided a wider operating window than the two-bed Berlin-type cycle, owing to pressure equalization and product-purge steps. The best overall performance was obtained for WGS syngas with the 1.6:1 AC:zeolite bed, reaching 99.5 vol.% H₂ at 84% recovery and maintaining 99.2 vol.% H₂ at 86% recovery. The tail gas was enriched in CO₂ up to approximately 72 vol.%, indicating potential for integration with downstream CO₂ management. Full article

To improve the efficiency, accuracy, and operational stability of postharvest potato tuber sorting in the presence of mineral impurities, mainly soil clods and stones, an intelligent sorting system for potato tubers and mineral impurities was designed and developed. The system employed YOLOv10n as the baseline network and incorporated a PSA module together with a dynamic blur augmentation strategy to establish a task-adapted detection model, termed YOLOv10n-PB. Rather than treating detection accuracy alone as the optimization objective, the proposed system jointly considered detection performance, inference-latency stability, temporal–spatial coordination, and pneumatic rejection reliability. In addition, a programmable logic controller and pneumatic actuators were integrated to enable online target identification and dynamic removal. Comparative experiments involving lightweight YOLO models and $L_{25}\left(5^3\right)$ orthogonal tests were conducted to evaluate the effects of conveyor belt speed, material spacing, and classification threshold on sorting performance. The results showed that YOLOv10n-PB achieved a mAP@0.5 of 98.9% on the test set. Among the investigated factors, conveyor belt speed had the greatest effect on overall sorting accuracy, followed by material spacing and classification threshold. Range analysis identified the optimal parameter combination as a conveyor belt speed of 0.2 m/s, a material spacing of 9 cm, and a classification threshold of 0.4. Validation experiments under these conditions yielded an overall sorting accuracy of 98.3%, a combined mineral-impurity removal accuracy of 98.3%, and a potato tuber false rejection rate of 1.7%. These results demonstrate the feasibility of the proposed system for accurate and stable automatic sorting of potato tubers and mineral impurities under postharvest operating conditions. Full article

In order to improve the accuracy of the lifting state detection of an oil–gas jack-up platform, a lifting state detection method based on improved random forest is proposed to solve the problems of low detection efficiency caused by the interference of lifting data and redundant features. To detect outliers in the lifting data of an oil–gas jack-up platform by the K-means clustering method and clean abnormal data, the principal component analysis method is introduced into the random forest algorithm to reduce the dimension of lifting data, and an improved random forest is constructed with the Gini impurity criterion to preliminarily classify the lifting state. Then, fuzzy comprehensive evaluation is used to refine the state of the classification result and realize lifting state detection. The test results show that the proposed method has good stability in lifting state timing detection, a high-inter class-to-intra-class distance ratio, and accurate platform displacement detection under different incident angles/motion responses. Full article

Zinc germanium phosphide (ZnGeP₂) is an important nonlinear crystal for mid-infrared conversion, but its performance is limited by residual absorption and intrinsic impurity phases. In this study, polycrystalline ZnGeP₂ was synthesized by a modified two-temperature method, purified by inclined directional recrystallization for up to three cycles, and then grown into single crystals by the vertical Bridgman method. The resulting material was examined by shadow-projection imaging, transmission spectroscopy in the 650–2500 nm range, absorption measurements at 2.097 µm, laser-induced damage threshold (LIDT) testing, and powder X-ray diffraction. Repeated purification improved optical homogeneity and near-infrared transparency, while the absorption coefficient at 2.097 µm decreased from 0.45 to 0.30 cm⁻¹ after three purification cycles. Semi-quantitative PXRD analysis showed progressive suppression of intrinsic impurity phosphides, with phase purity increasing from 86.31% after the first cycle to 95.995% after the second and reaching 100% after the third within the detection limit of the method. However, the LIDT decreased with increasing purification number, indicating a trade-off between lower optical losses and damage resistance. These results demonstrate that inclined directional recrystallization is an effective pre-growth purification route for ZnGeP₂ and that the optimal number of purification cycles should be selected according to the intended application. Full article

This study presents a Scanning Electron Microscopy and Energy-Dispersive X-ray Spectroscopy (SEM/EDS) characterization of three American Civil War era ammunition: the .56-52 Spencer, .56-56 Spencer, and .50 US carbine centerfire. Analysis revealed the Spencer rimfire cases consist of pure copper, likely to prevent the embrittlement caused by mercury fulminate in the primer, whereas the latter .50 US carbine centrefire case utilizes a brass alloy. The propellant was confirmed to be traditional black powder. Notably, traces of silicon and aluminum detected within the primer and propellant residues were thoroughly investigated. The lack of systematic glass markers suggests these elements originated from impurities or degraded organic binders, rather than intentionally added glass frictionators. Ultimately, this research addresses a gap in the literature regarding the material composition and degradation of mid-19th-century ammunition. Full article

Down quality is the core evaluation indicator of thermal insulation products, and its grade determination strictly complies with the down content index specified in the national standard GB/T 17685-2016 Feather and Down. Traditional down quality inspection adopts manual sorting and weighing methods, which are plagued by low efficiency, strong subjectivity and high error rates, thereby restricting the intelligent upgrading of the down industry. This study aims to develop an automatic down detection and quantitative grading method conforming to national standards based on deep learning. A down dataset consisting of 632 RGB images is constructed, with each image containing 5–10 individual down samples and covering five categories: mature down clusters, immature down clusters, down filaments, feathers, and yellow-tail down. Three mainstream frameworks including YOLOv8, YOLOv11 and YOLOv26 are trained for performance comparison. Precision, recall, mAP@50 and mAP@50-95 are adopted as evaluation metrics. In addition, this paper proposes a research idea for down content calculation and automatic classification and grading of down quality in accordance with relevant national standards. The experimental results demonstrate that the latest models do not necessarily achieve the optimal performance. The newly released YOLOv26n and YOLOv26m exhibit relatively low accuracy in the down detection task, with mAP@50 values of only 0.98556 and 0.99077, and recall rates of 0.95032 and 0.97848, respectively, failing to outperform their previous-generation counterparts. In contrast, YOLOv11n achieves the best comprehensive performance, with an mAP@50 of 0.99416, a precision of 0.99544, a recall of 0.99722, and an mAP@50-95 of 0.63464. Meanwhile, the model has only 2.58 M parameters, a computational complexity of 6.3 GFLOPs, and a single training time of approximately 6.7 min, achieving an optimal balance between detection accuracy and computational efficiency. All models show the highest detection accuracy for mature down clusters and yellow-tailed down, while slight confusion exists between immature down clusters and down filaments. This study verifies the feasibility of the YOLO series models in down quality inspection in accordance with national standards, and reveals that model architecture iteration does not necessarily lead to performance improvement on specific industrial datasets. The lightweight and robustly designed YOLOv11n presents greater practical value. The intelligent detection scheme proposed in this paper can assist in optimizing the traditional manual quality inspection workflow, alleviating the burden of manual counting and reducing subjective errors. It provides new ideas and technical references for the rapid screening and objective determination of down quality. Furthermore, the proposed research framework for automatic classification and grading of down quality is expected to promote the development of down quality inspection toward standardization, intelligence, and automation in the future. Full article

Niobium commonly occurs as a minor component in Fe–Ti–O oxide systems associated with ilmenite ores and titanium-bearing metallurgical materials, yet its speciation and incorporation mechanisms remain insufficiently resolved. This study investigates the distribution, structural incorporation, and microphase localization of niobium in the Fe–Ti–O system, with emphasis on TiO₂-rich domains. Electron probe microanalysis with EDS/WDS, X-ray diffraction, thermal analysis, and thermodynamic modeling in HSC Chemistry were combined to characterize niobium-bearing phases in natural and model oxide systems. Niobium was found to occur in two principal modes: as a low-level isomorphic impurity in Fe–Ti oxide matrices and as localized enrichments in TiO₂-rich domains, particularly rutile lamellae. A first-order area-based estimate for representative analyzed grains suggests that approximately 60–80% of the detected niobium is associated with the lamellar TiO₂ channel. The combined observations are consistent with a sequential mechanism involving isomorphic substitution of Nb in Ti sites, followed by microphase enrichment and segregation into more compositionally distinct niobium-bearing oxide or titanate microphases. In the studied material, integrated mapped-field Nb is about 0.04 wt.%, whereas matrix Nb commonly lies at trace levels of about 0.02–0.05 wt.% under the applied analytical conditions, consistent with low-level background incorporation, whereas locally Nb-enriched rutile-like domains reach about 0.70–1.00 wt.%. TiO₂-rich domains are therefore identified as the principal concentrators of niobium in Fe–Ti oxide systems. Taken together, the natural observations, model experiments, and thermodynamic calculations support an integrated mechanistic sequence of Nb evolution in the Fe–Ti–O system: isomorphic substitution → microphase enrichment in TiO₂-related domains → segregation into distinct Nb-bearing oxides/niobates. These findings provide a practical framework for interpreting Nb behavior in natural and technological Fe–Ti–O materials. Full article

Metal-organic frameworks (MOFs), particularly ELM-11, are promising sorbents for CO₂ capture due to their gate-opening phenomenon and excellent reusability. Since actual exhaust gases contain impurities such as NO₂, in this study, the effect of NO₂ on the CO₂ sorption performance of ELM-11 was investigated. ELM-11 was exposed to 1000 ppm NO₂ for varying durations, ranging from short to long, and subsequent CO₂ sorption was evaluated using several methods: gravimetric analysis (TG-DTA), volumetric analysis (sorption isotherms), FT-IR spectroscopy (to detect chemical bond changes), TG-MS (to analyze decomposition products), and PXRD (to observe structural changes). The TG-DTA results indicated that long-term NO₂ exposure (e.g., 20 h) generally reduced CO₂ sorption, whereas short-term exposure (3 h) could enhance it. This finding was supported by volumetric sorption isotherm measurements. FT-IR and TG-MS analyses revealed that NO₂ underwent both physical and chemical sorption in small amounts, with chemical sorption occurring through reactions with Cu²⁺ ions. Consequently, 20 h of NO₂ exposure resulted in approximately a 6 or 10% reduction in CO₂ recovery capacity. However, since the degradation was only 6 or 10% despite exposure to a relatively high concentration of NO₂ (1000 ppm), these results suggest that ELM-11 exhibits high resistance to NO₂, making it suitable for practical applications. Full article

To enhance the precision of cottonseed impurity detection and address issues such as high miss-detection rates and suboptimal performance, this paper introduces an improved YOLOv11 algorithm, termed YOLOv11-SMS. Initially, the algorithm integrates a local self-attention mechanism (LRSA) to design the C2PSA-SL module, which augments the model’s ability to learn local information while maintaining global feature awareness. Furthermore, the feature extraction stage and the network head incorporate a multi-branch reparameterized convolution (MBRConv) module, enhancing feature extraction capabilities while preserving the model’s lightweight properties. Lastly, a spatial adaptive modulation (SAFM) module is introduced to optimize the detection of small targets. Experimental results demonstrate that YOLOv11-SMS outperforms the baseline model, with mAP@_50–95 increasing from 79.42% to 82.49%, an improvement of 3.07 percentage points. The average mIOU increased from 90.98% to 94.18%, representing a 3.2 percentage point improvement. Moreover, the model achieves an impressive real-time inference speed of 178.63 frames per second (FPS), effectively balancing detection accuracy and speed, offering an efficient and precise solution for cottonseed impurity detection. Full article

In this study, the carbothermic reduction of sodium antimonate in crucible smelting was investigated. The optimal process temperature was determined to be 900 °C, with 10% coke consumption (with an ash content up to 15.33%) and a feed particle size of minus 1 mm. The process does not involve the addition of slag-forming components. Sodium participates in the formation of the slag phase. According to the smelting results, the amount of antimony recovered as crude metal reached 71–72%, while the Sb content in the crude metal reached up to 94.5%. A significant portion of antimony (up to 27%) volatilizes with off-gases. A notable sodium content was detected in the crude antimony, reaching up to 8% in some samples, while more than 80% of sodium was transferred to the slag phase. Arsenic, present in the initial concentrate at a level of 0.6%, was distributed approximately equally among the metallic, slag, and gas phases. Lead was predominantly concentrated in the crude antimony. Iron preferentially dissolved in the crude antimony. Other impurities were distributed in comparable amounts between the metallic and slag phases. Tellurium, present in sodium antimonate at 0.79%, was detected in some samples within the slag phase. Full article