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Keywords = chemical experiment optimization

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25 pages, 2360 KB  
Article
ACF-YOLO: Feature Enhancement and Multi-Scale Alignment for Sustainable Crop Small Object Detection
by Chuanxiang Li, Yihang Li, Wenzhong Yang and Danny Chen
Sustainability 2026, 18(9), 4168; https://doi.org/10.3390/su18094168 - 22 Apr 2026
Abstract
Sustainable precision agriculture is crucial for optimizing resource utilization, reducing chemical inputs, and ensuring global food security. High-precision automatic recognition and monitoring of key crop organs (e.g., wheat heads and flower clusters) serve as the technological foundation for sustainable agricultural management decisions. However, [...] Read more.
Sustainable precision agriculture is crucial for optimizing resource utilization, reducing chemical inputs, and ensuring global food security. High-precision automatic recognition and monitoring of key crop organs (e.g., wheat heads and flower clusters) serve as the technological foundation for sustainable agricultural management decisions. However, visual perception in natural field environments is highly susceptible to external conditions. To address the challenges of severe background interference and feature dilution in crop small object detection within complex agricultural scenarios, this paper proposes an enhanced detection network, ACF-YOLO, based on YOLO11. First, an Aggregated Multi-scale Local-Global Attention (AMLGA) module is designed to enhance the feature representation of weak targets by fusing local details with global semantics. Second, a Context-Guided Fusion Module (CGFM) and a Soft-Neighbor Interpolation (SNI) strategy are introduced. Their synergy alleviates feature aliasing effects and ensures the precise alignment of deep semantic information with shallow spatial details. Furthermore, the Inner-MPDIoU loss function is employed to optimize the bounding box regression accuracy for non-rigid targets by incorporating geometric constraints and auxiliary scale factors. To verify the detection capability of the proposed method, we constructed a UAV Wheat Head Dataset (UWHD) and conducted extensive experiments on the UWHD, GWHD2021, and RFRB datasets. The experimental results demonstrate that ACF-YOLO outperforms other comparative methods, confirming its stable detection performance and contributing to the sustainable development of agriculture. Full article
(This article belongs to the Section Sustainable Agriculture)
17 pages, 1878 KB  
Article
QSAR Models for Repeated Dose Toxicity in Rats Using the CORAL Software
by Alla P. Toropova, Andrey A. Toropov, Nadia Iovine, Gianluca Selvestrel, Alessandra Roncaglioni and Emilio Benfenati
Toxics 2026, 14(4), 338; https://doi.org/10.3390/toxics14040338 - 17 Apr 2026
Viewed by 270
Abstract
The evaluation of the safety of chemical substances requires the identification of a safe dose, which has no adverse effects on humans. This is obtained through animal studies, with exposure prolonged for months. Repeated-dose toxicity is a term in toxicology and pharmacology referring [...] Read more.
The evaluation of the safety of chemical substances requires the identification of a safe dose, which has no adverse effects on humans. This is obtained through animal studies, with exposure prolonged for months. Repeated-dose toxicity is a term in toxicology and pharmacology referring to the highest tested dose of a substance, so-called No Observed Adverse Effect Level (NOAEL). Experimental data on NOAEL taken from the literature and the OpenFoodTox database (total n = 848). To speed up the processing of the enormous number of substances we are exposed to, in silico models are an attractive solution. Monte Carlo technique, incorporating the Las Vegas algorithm, was applied to develop models for repeated-dose toxicity in rats. Optimal descriptors were calculated using correlation weights for attributes of the Simplified Molecular Input Line Entry System (SMILES). Computational experiments were conducted 5 times, with splits obtained using the Las Vegas algorithm. Good predictive potential was observed for these models, with an average determination coefficient on the validation set of 0.77 ± 0.04. Full article
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16 pages, 795 KB  
Article
The Effect of Organic and Mineral Fertilizers on Silage Maize Biomass Yield and Quality Across Different Soil–Climate Conditions in the Czech Republic
by Lukáš Hlisnikovský, Ladislav Menšík, Muhammad Roman, Jaffar Iqbal, Veronika Zemanová, David Kincl and Pavel Nerušil
Plants 2026, 15(8), 1231; https://doi.org/10.3390/plants15081231 - 16 Apr 2026
Viewed by 242
Abstract
Maize biomass production and quality are influenced by numerous factors, including fertilization, soil characteristics, and climatic conditions. The aim of our study was to evaluate how different fertilization treatments ((1) Control, (2) farmyard manure (FYM), (3) FYM with added mineral nitrogen (FYM + [...] Read more.
Maize biomass production and quality are influenced by numerous factors, including fertilization, soil characteristics, and climatic conditions. The aim of our study was to evaluate how different fertilization treatments ((1) Control, (2) farmyard manure (FYM), (3) FYM with added mineral nitrogen (FYM + N), and (4) FYM with added NPK mineral fertilizers (FYM + NPK)) affect the biomass yield and quality parameters (crude protein (CP), fiber content (FC), neutral detergent fiber (NDF), starch content (STR), organic matter digestibility (OMD), and neutral detergent fiber digestibility (DNDF)) of silage maize under various soil and climatic conditions in the Czech Republic (Caslav—degraded Chernozem, Ivanovice na Hané–Chernozem, Lukavec–Cambisol). The experiment was conducted from 2020 to 2023. Additionally, the study analyzed the effects of fertilization on soil chemical properties (pH, P, K, Ca, Mg, C, N). The highest average biomass yields were recorded in Ivanovice (23.8 t ha−1, A), followed by Lukavec (19.7 t ha−1, B) and Caslav (18.1 t ha−1, B). Comparing fertilizer treatments, no significant differences were observed among FYM, FYM + N, and FYM + NPK; however, all three treatments significantly outperformed the Control at all sites. Conversely, fertilization did not affect the quality parameters. For silage maize, FYM represents the optimal fertilization strategy, providing yields and quality comparable to the combined application of mineral N, P, and K, which are more costly (in terms of purchase and application) and, under certain conditions, may negatively impact the environment. Nevertheless, the application of mineral fertilizers increased soil nutrient content, thereby improving conditions for subsequent crops. Full article
(This article belongs to the Section Plant–Soil Interactions)
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19 pages, 6056 KB  
Article
A Novel Pressure-Assisted Induction Melting Technique for Synthesis of Lightweight High-Entropy Alloys: A Concept, Process Development and Hardware Design
by Peter Newcombe and Frank Czerwinski
Materials 2026, 19(8), 1588; https://doi.org/10.3390/ma19081588 - 15 Apr 2026
Viewed by 287
Abstract
Lightweight high-entropy alloys are primarily designed to overcome the strength-to-density ratio limitations of conventional counterparts and often consist of elements with drastically different melting temperature and vapor pressure. Their chemistry, therefore, imposes challenges on alloy synthesis, particularly through liquid metal engineering routes, since [...] Read more.
Lightweight high-entropy alloys are primarily designed to overcome the strength-to-density ratio limitations of conventional counterparts and often consist of elements with drastically different melting temperature and vapor pressure. Their chemistry, therefore, imposes challenges on alloy synthesis, particularly through liquid metal engineering routes, since elements with high vapor pressure (e.g., Mg, Zn, Li) vaporize before the higher-melting-point ingredients (e.g., Cu, V, Ni) are fully molten, resulting in volatile element loss. To overcome this challenge, a novel pressure-assisted induction melting (PAIM) process was developed and the proprietary furnace for its implementation was designed and built. The system allows precision melting of up to 10 cm3 of an alloy at temperatures up to 1700 °C while addressing the partial pressure requirements during the melting progress. The chamber is prepared using rough vacuum and re-filled with inert gas such as argon with the operating pressure range from about 10−4 MPa up to maximum of 1.6 MPa (233 psi). The alloy chemical composition can be modified in situ by feeding solid additives at specific melting stages through the isolated airlock without disrupting the pressure conditions within the chamber. The viability of the concept was verified by synthesis of two lightweight non-equimolar high-entropy alloys: Mg-rich Mg50(MnAlZnCu)50 and Al-rich Al35Mg30Si13Zn10Y7Ca5. The experiments showed that sequential multi-step melting procedures, designed based on inputs from FactSage computational analysis, when combined with PAIM synthesis, allowed manufacturing fully dense and chemically homogenous complex alloy compositions with optimal volumes for materials discovery research. Full article
(This article belongs to the Section Metals and Alloys)
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25 pages, 3853 KB  
Article
The Combined Application of Organic Fertilizer and Chemical Fertilizer Increases Alfalfa Yield, Enhances Soil Nutrient Availability, and Improves Soil Biological Properties
by Xuerong Ma, Lan Wang, Zhuang Xue, Qi Wang, Yihan Qian, An Yan and Lu Cai
Agronomy 2026, 16(8), 795; https://doi.org/10.3390/agronomy16080795 - 13 Apr 2026
Viewed by 320
Abstract
This study focused on alfalfa (Medicago sativa cv. Xinmu No. 4) as the experimental material, and a two-year field plot controlled experiment was conducted to compare the effects of different co-application ratios of organic and chemical fertilizers on alfalfa yield, soil nutrient [...] Read more.
This study focused on alfalfa (Medicago sativa cv. Xinmu No. 4) as the experimental material, and a two-year field plot controlled experiment was conducted to compare the effects of different co-application ratios of organic and chemical fertilizers on alfalfa yield, soil nutrient status, and soil biological characteristics. The six fertilization treatments were as follows: CM0 (100% cattle manure), CM1 (75% cattle manure + 25% chemical fertilizer), CM2 (50% cattle manure + 50% chemical fertilizer), CM3 (25% cattle manure + 75% chemical fertilizer), CM4 (100% chemical fertilizer), and CK (no fertilizer application). The results showed that alfalfa hay yield was highest under the CM3 treatment in both 2024 and 2025, representing increases of 38.03% and 40.85%, respectively, compared with the control (CK). Relative to the other treatments, CM3 significantly increased soil total nitrogen, alkali-hydrolyzable nitrogen, available phosphorus, readily available potassium, and organic matter contents. In addition, CM3 markedly enhanced the activities of soil nitrate reductase (NR), nitrite reductase (NiR), and the root enzymes glutamate synthase (GOGAT) and glutamine synthase (GS). The combined application of organic and chemical fertilizers significantly reshaped the soil bacterial community structure associated with alfalfa. Under the CM3 treatment, Chao1, Shannon, and ACE indices of soil bacterial diversity increased, whereas the Simpson index decreased. Moreover, the CM3 treatment was associated with higher relative abundances of several key bacterial phyla and genera. The 25% cattle manure plus 75% chemical fertilizer (CM3) treatment exhibited the strongest overall effects, significantly increasing total alfalfa hay yield, enhancing soil macronutrient availability and key enzyme activities, improving soil microbial α-diversity, and optimizing soil bacterial community structure. This treatment consistently outperformed the no-fertilizer control (CK) and all other organic–inorganic fertilizer combinations. Collectively, these findings provide robust scientific evidence supporting strategies to increase forage productivity, mitigate environmental impacts, and promote the sustainable development of the grassland industry. Full article
17 pages, 2217 KB  
Article
Beyond Conventional Methods: Rapid and Precise Quantification of Polyphenols in Vigna umbellata via Hyperspectral Imaging Enhanced by Multi-Scale Residual CNN
by Hao Liang, Xin Yang, Nan Wang, Xinyue Lu, Wenwu Zou, Aicun Zhou, Xiongwei Lou and Yufei Lin
Sensors 2026, 26(8), 2356; https://doi.org/10.3390/s26082356 - 11 Apr 2026
Viewed by 434
Abstract
Vigna umbellate, a typical edible and medicinal crop, is rich in polyphenolic compounds with antioxidant, antibacterial, anti-inflammatory, and lipid-regulating activities. However, traditional methods for polyphenol content detection rely on chemical analysis, which is cumbersome and time-consuming, making it difficult to meet the [...] Read more.
Vigna umbellate, a typical edible and medicinal crop, is rich in polyphenolic compounds with antioxidant, antibacterial, anti-inflammatory, and lipid-regulating activities. However, traditional methods for polyphenol content detection rely on chemical analysis, which is cumbersome and time-consuming, making it difficult to meet the demands of high-throughput rapid detection. Although hyperspectral imaging technology offers the potential for non-destructive and rapid detection, existing analytical methods are often limited by issues such as high spectral band redundancy, insufficient feature extraction, and inadequate model stability, which constrain prediction accuracy and practical application potential. To address this, this study proposes a multi-scale residual convolutional neural network (MS-RCNN) based on competitive adaptive reweighted sampling (CARS) for feature band selection, combined with near-infrared hyperspectral imaging technology, to construct a rapid and non-destructive prediction model for the polyphenol content of Vigna umbellata. The model employs a parallel multi-scale convolutional module to extract spectral features with different receptive fields, and incorporates residual connections and adaptive pooling mechanisms to enhance feature reuse and robustness. Experiments compared the performance of partial least squares regression (PLSR), least squares support vector machine (LS-SVM), multi-scale convolutional neural network (MS-CNN), and MS-RCNN models. The results indicate that the MS-RCNN model based on CARS screening achieved the best prediction performance, with a coefficient of determination (R2) of 0.9467, a root mean square error of prediction (RMSEP) of 0.0448, and a residual predictive deviation (RPD) of 4.33. Compared with the optimal PLSR and LSSVM models, its R2 values were improved by 0.2078 and 0.1119, respectively. In summary, the MS-RCNN model proposed in this study enables rapid, non-destructive, and accurate prediction of polyphenol content in Vigna umbellata, providing an efficient technical approach for quality detection of edible and medicinal crops. Full article
(This article belongs to the Special Issue Spectroscopy and Sensing Technologies for Smart Agriculture)
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30 pages, 3526 KB  
Article
Development of an Assay for C13-Norisoprenoid Analysis in Riesling Wine and Its Application to Simulated Aging by Acidic Hydrolysis Using Response Surface Methodology
by Sebastian Scharf, Lara Preuß, Peter Winterhalter and Recep Gök
Analytica 2026, 7(2), 29; https://doi.org/10.3390/analytica7020029 - 9 Apr 2026
Viewed by 178
Abstract
C13-Norisoprenoids are important contributors to the aroma of Riesling wine. Their quantification is analytically challenging due to their low concentrations, the lack of commercial standards and their pronounced sensitivity to analytical conditions, reflecting their chemical lability, as well as the dynamic [...] Read more.
C13-Norisoprenoids are important contributors to the aroma of Riesling wine. Their quantification is analytically challenging due to their low concentrations, the lack of commercial standards and their pronounced sensitivity to analytical conditions, reflecting their chemical lability, as well as the dynamic nature of the wine matrix, leading to high reactivity and, consequently, remarkable structural diversity. Here, we developed an assay for the analysis of C13-norisoprenoids in wine using headspace solid-phase microextraction coupled to gas chromatography–mass spectrometry (HS-SPME–GC-MS/MS). After evaluating different fiber materials, a statistical design of experiments (DoE) approach was employed to systematically optimize key HS-SPME parameters, including incubation, extraction and desorption conditions. Selected reaction monitoring (SRM) transitions were established for all targeted C13-norisoprenoids, allowing the assay to provide relative quantification of more than 40 compounds using representative labeled and unlabeled standards to generate linear calibration curves. Following method validation, this approach was applied to a young German Riesling wine to investigate the effect of various acidic hydrolysis conditions on the norisoprenoid profile as well as on specific compounds. A central composite design (CCD) was used to systematically study the impact of pH, temperature, and hydrolysis time. Quantitative data were obtained for 22 C13-norisoprenoids demonstrating that hydrolysis conditions strongly affected the norisoprenoid composition. pH and temperature showed a greater influence than reaction time. Response surface models (RSM) indicated that TDN, Vitispirane and TPB in particular are predominantly formed under strongly acidic and high-temperature conditions, whereas others such as Riesling acetal and actinidols are formed under milder conditions. The results indicate that hydrolysis conditions should be tailored to the specific norisoprenoid under investigation and the research question, particularly when simulating conditions of accelerated wine ageing for analytical purposes. Full article
(This article belongs to the Section Sample Pretreatment and Extraction)
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16 pages, 5067 KB  
Article
Modeling of Water Quality in Deep Tunnels Coupling Temperature–Depth Effects
by Xiaomei Zhang, Qingmin Zhang, Yuanjing Yang, Yuntao Guan and Rui Chen
Appl. Sci. 2026, 16(8), 3664; https://doi.org/10.3390/app16083664 - 9 Apr 2026
Viewed by 234
Abstract
As large-scale underground storage infrastructure, deep tunnels exhibit distinct water quality dynamics driven by ground temperature gradients. Currently, there is limited investigation into water quality modeling for deep tunnel systems. Unraveling the correlation between temperature–depth gradients and water quality evolution is crucial for [...] Read more.
As large-scale underground storage infrastructure, deep tunnels exhibit distinct water quality dynamics driven by ground temperature gradients. Currently, there is limited investigation into water quality modeling for deep tunnel systems. Unraveling the correlation between temperature–depth gradients and water quality evolution is crucial for the operation and management of such systems. In this study, field experiments were carried out in the Qianhai–Nanshan Deep Tunnel to investigate complex water quality behavior, leading to the development of chemical oxygen demand (COD) and ammonia nitrogen (NH3–N) models that incorporate temporal variation, temperature, and burial depth. Results indicate that temperature is the dominant factor influencing water quality in deep tunnel storage. Increased ground temperature promotes the degradation and mass transport of pollutants within the tunnel system. Owing to temperature–depth effects, the deeply buried Qianhai tunnel significantly reduces river discharge pollution after water storage, with COD and NH3–N removal rates reaching 74.9% and 26.8%, respectively. Temperature-controlled experiments showed that COD and NH3–N reduction rates varied between 60–94% and 10–30% across a temperature range of 20–34 °C. The proposed model was validated against experimental data, achieving Nash–Sutcliffe efficiency coefficients of 0.7–0.8. This study provides a methodological foundation for simulating complex aquatic environments and offers a decision-support tool for optimizing the operational strategies of deep tunnel systems. However, the model’s current generalization capability is constrained by the limited experimental conditions (20–34 °C, 12 days) and the lack of experimental replicates, which should be systematically addressed in future studies. Full article
(This article belongs to the Special Issue Environmental Issues in Geotechnical Engineering)
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13 pages, 919 KB  
Article
Inactivation of Weedy Rice Using 915 MHz Microwaves with Soil Physicochemical Property and Microbiome Retention
by Kaushik Luthra, Devisree Chukkapalli, Bindu Regonda, Chris Isbell, Akshita Mishra and Griffiths Atungulu
AgriEngineering 2026, 8(4), 140; https://doi.org/10.3390/agriengineering8040140 - 5 Apr 2026
Viewed by 268
Abstract
There is a growing demand for alternative low cost and sustainable weed management technology suitable for aerobic and organic farming. This study evaluates 915 MHz microwave heating as a potential non-chemical approach for managing weedy rice while assessing its impact on soil physicochemical [...] Read more.
There is a growing demand for alternative low cost and sustainable weed management technology suitable for aerobic and organic farming. This study evaluates 915 MHz microwave heating as a potential non-chemical approach for managing weedy rice while assessing its impact on soil physicochemical properties and selected microbial groups. Microwave power levels of 10, 20, and 30 kW were applied to soil at depths of 2.5, 8.9, and 15.2 cm under controlled laboratory conditions. Weed emergence was quantified using the total germinability index (TGI), and soil physicochemical and microbial responses were analyzed in separate experiments. TGI decreased significantly with increasing microwave power and decreasing soil depth, ranging from 0.84 (10 kW at 15.2 cm) to 0 (20 kW at 2.5 cm and 30 kW at 8.9 cm). For 8.9 cm soil depth, energy levels between 176 and 265 kJ/kg resulted in 80–100% emergence suppression, while treatment of 15.2 cm soil at 30 kW for 30 s (188 kJ/kg) reduced TGI by approximately 80% and germination by 64% relative to control. Soil physicochemical properties showed minimal changes, with values remaining within agronomically acceptable ranges. Total bacterial abundance was not significantly affected, whereas ammonia-oxidizing archaea and bacteria were reduced following treatment. These results indicate that microwave heating can effectively suppress weedy rice emergence under controlled conditions, primarily through thermal effects. However, TGI reflects emergence suppression and does not distinguish underlying mechanisms such as lethality, injury, or dormancy. Additionally, limitations including low replication, lack of depth-matched controls, and limited spatial temperature measurements should be considered. Further field-scale studies are needed to validate performance, optimize energy requirements, and assess long-term soil impacts. Full article
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28 pages, 1836 KB  
Article
Radiation-Induced Changes in Antibiotic Residues, Amino Acid Profiles, and Fatty Acid Composition of Poultry Meat Under Electron-Beam Irradiation: Implications for Sustainable Food Production
by Raushangul Uazhanova, Igor Danko, Maxat Iztileuov, Gaukhar Jamanbayeva and Maxat Toishimanov
Agriculture 2026, 16(7), 796; https://doi.org/10.3390/agriculture16070796 - 3 Apr 2026
Viewed by 394
Abstract
The increasing occurrence of antibiotic residues in poultry meat represents a serious food safety concern associated with antimicrobial resistance and potential risks to human health. This study investigated the effects of electron beam irradiation on antibiotic residues and nutritional quality parameters of poultry [...] Read more.
The increasing occurrence of antibiotic residues in poultry meat represents a serious food safety concern associated with antimicrobial resistance and potential risks to human health. This study investigated the effects of electron beam irradiation on antibiotic residues and nutritional quality parameters of poultry meat. All experiments and data collection were carried out in 2025. Fresh poultry samples were irradiated using an ILU-10 pulsed linear electron accelerator at doses of 2, 4, 6, 8, and 10 kGy. Antibiotic residues were determined by HPLC-DAD, amino acid composition was analyzed using HPLC, and fatty acid profiles were evaluated by gas chromatography. Electron beam irradiation produced significant dose-dependent changes in the chemical composition of poultry meat. Total amino acid content decreased progressively with increasing irradiation dose, with reductions of up to 60–73% at 10 kGy depending on tissue type. Branched-chain and essential amino acids showed similar trends. Fatty acid analysis revealed a shift toward higher proportions of saturated fatty acids and a decline in monounsaturated and polyunsaturated fatty acids. The PUFA/SFA ratio decreased from 0.48 in control samples to 0.25 at 10 kGy. Tetracycline residues were not detected in any samples, whereas chloramphenicol residues were present in control meat but were progressively reduced after irradiation and became undetectable at doses ≥ 8 kGy. These results demonstrate that electron beam irradiation can effectively reduce antibiotic residues in poultry meat; however, higher irradiation doses may significantly alter amino acid and lipid composition. Therefore, optimization of irradiation parameters is necessary to balance improvements in food safety with the preservation of nutritional quality for the production of safe and sustainable food products. Optimization of irradiation parameters is therefore necessary to balance food safety benefits with preservation of nutritional quality. Furthermore, this research contributes to the achievement of Sustainable Development Goal (SDG) 2, while the obtained results also support SDG 3 by promoting safer food systems and protecting public health. Full article
(This article belongs to the Section Agricultural Product Quality and Safety)
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19 pages, 4907 KB  
Article
DOE-Based Optimization of Dietary Fiber Extraction Process and Bioactivity Evaluation of Plum (Prunus salicina Lindl.) Processing By-Products
by Juan Chen, Xueting Zhang, Xin Hu, Yan Wen, Dongyan Huang, Xiaoyu Wen, Guiqun Song, Qi Yuan and Xudong Liu
Foods 2026, 15(7), 1199; https://doi.org/10.3390/foods15071199 - 2 Apr 2026
Viewed by 321
Abstract
Plum pomace (PP), a key by-product of plum juice processing, is a rich yet underutilized source of dietary fiber. However, its high-value exploitation is severely limited by the lack of efficient extraction and modification technologies. This study optimized the extraction of soluble dietary [...] Read more.
Plum pomace (PP), a key by-product of plum juice processing, is a rich yet underutilized source of dietary fiber. However, its high-value exploitation is severely limited by the lack of efficient extraction and modification technologies. This study optimized the extraction of soluble dietary fiber (SDF) and insoluble dietary fiber (IDF) from plum pomace (PP) via Design of Experiments (DOE), and evaluated their modification effects. Alkaline extraction was screened as the optimal method for IDF, and orthogonal experiments determined the optimal conditions: solid-to-liquid ratio 1:20 g/mL, 14 g/L NaOH, 60 °C, and 80 min, achieving a high extraction yield of 62.18%. For SDF, enzymatic extraction was superior, and response surface methodology (RSM) optimized the process to a solid-to-liquid ratio of 1:15.5, 1.0% enzyme dosage, 61.5 °C, and 92 min, with a yield of 29.3%. Physical, chemical, and biological modifications all significantly enhanced SDF’s water/oil-holding capacity, cholesterol/glucose adsorption capacity, and cation exchange capacity. Biologically modified SDF showed the most significant enhancement, with WHC of 5.58 ± 0.05 g/g, OHC of 4.38 g/g, CAC of 7.68 mg/g, and CEC of 3.28 mmol/g. These results provide technical support for the high-value utilization of PP and lay a foundation for its application in functional foods and nutraceuticals. Full article
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24 pages, 7143 KB  
Article
Spectroscopic Insights into Nanodiamond–Doxorubicin Interactions in Drug Delivery Systems for Potential Cancer Treatment: “What Is Essential Is Invisible to the Eye”
by Danica Jović, Branislav Jović, Ivana Borišev, Višnja Bogdanović and Aleksandar Djordjevic
Pharmaceutics 2026, 18(4), 438; https://doi.org/10.3390/pharmaceutics18040438 - 1 Apr 2026
Viewed by 475
Abstract
Background/Objectives: Non-covalent nanocarrier-based systems have become a promising platform as they offer a strategy to improve the efficacy-safety profile of doxorubicin (DOX) without altering its chemical structure. Praised for biocompatibility and rich surface chemistry, nanodiamonds (NDs) have launched as nanocarriers of choice [...] Read more.
Background/Objectives: Non-covalent nanocarrier-based systems have become a promising platform as they offer a strategy to improve the efficacy-safety profile of doxorubicin (DOX) without altering its chemical structure. Praised for biocompatibility and rich surface chemistry, nanodiamonds (NDs) have launched as nanocarriers of choice for advanced cancer therapy. By investigating DOX-ND physicochemical interactions, this work advances the structural understanding of a non-covalent potential anticancer system, which has not been quantitatively experimentally explored so far. Methods: To our knowledge, this is among the first studies combining ultraviolet–visible (UV–VIS) spectroscopy with spectral deconvolution to reveal the redistribution of different DOX species in the presence of NDs. Centrifugation-assisted analysis enabled differentiation between hypothetical labile and stable ND/DOX fractions. Adsorption kinetics was studied, and dynamic light scattering (DLS) measured particle size and zeta potential. In vitro screening was performed in non-malignant fibroblasts (MRC-5) and malignant melanoma (HS294T), glioblastoma (U251), and breast cancer (MCF-7) cells to evaluate ND/DOX combinations. Results: Centrifugation analysis revealed heterogeneous ND-DOX binding. Kinetic experiments showed fast multi-stage adsorption kinetics, best described by a bi-exponential decay function and the Weber–Morris model. DLS suggested stable systems with a particle size within 10–80 nm, predominantly around 20 nm, and positive zeta potential. Comparative in vitro screening demonstrated differential cellular responses across cell types, highlighting the relevance of ND/DOX interactions. Conclusions: The findings contribute to elucidating ND-DOX interactions relevant for the design and optimization of drug delivery systems, emphasizing the importance of spectroscopic insights for the design of nanodiamond-based drug delivery systems. Full article
(This article belongs to the Special Issue Carbon-Based Nanomaterials for Pharmaceutical Applications)
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19 pages, 1449 KB  
Article
Study on the Injection Modes and Displacement Characteristics of Chemical Compound Flooding in Heavy Oil Reservoirs After Multiple Cycles of Huff-and-Puff
by Li Zhang, Lei Tao, Guanli Xu and Jiajia Bai
Energies 2026, 19(7), 1728; https://doi.org/10.3390/en19071728 - 1 Apr 2026
Viewed by 348
Abstract
The chemical agent injection modes and displacement characteristics of chemical compound flooding, consisting of a plugging agent, an oil displacement agent, and a viscosity reducer, were investigated by laboratory experiments for target heavy oil reservoirs after multiple cycles of huff-and-puff. The performances of [...] Read more.
The chemical agent injection modes and displacement characteristics of chemical compound flooding, consisting of a plugging agent, an oil displacement agent, and a viscosity reducer, were investigated by laboratory experiments for target heavy oil reservoirs after multiple cycles of huff-and-puff. The performances of the oil displacement agent, viscosity reducer and plugging agent were evaluated, and the formulation and concentration were optimized. The oil displacement effects and displacement characteristics of different injection modes were studied by sand-filled two-pipe models. The experiment results showed that alternating injections of the oil displacement agent and viscosity reducer yielded better results than their mixed injection, and small segments alternating injections achieved the highest recovery. The larger the dosage of the oil displacement agent, the larger the maximum liquid production ratio between the high- and low-permeability layers, but with the smaller the liquid production reverse duration. The larger the dosage of the viscosity reducer, the greater the water cut decrease but the smaller the maximum liquid production ratio. For chemical compound flooding in the Zhong’er block in the Gudao oilfield, the recommended injection mode was 0.1 PV plugging agent + 2000 mg/L of oil displacement agent + 0.5% viscosity reducer, with small segments of the oil displacement agent being followed by a viscosity reducer at an injection slug ratio of 6:4. However, the injection mode depends on the prices of oil and the chemical agent. When prices fluctuate, the chemical agent concentration should be adjusted accordingly. Full article
(This article belongs to the Special Issue Petroleum and Natural Gas Engineering: 2nd Edition)
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17 pages, 3091 KB  
Article
Ultrasound-Assisted Extraction of Polyphenols from Hericium erinaceus: Optimization, Bioactivities and LC-MS-Based Chemical Profiling
by Hongfei Liu, Cong Zhao, Shuyue Pang, Yuting Shu, Lina Chen, Jing Wang and Helong Bai
Molecules 2026, 31(7), 1138; https://doi.org/10.3390/molecules31071138 - 30 Mar 2026
Viewed by 429
Abstract
In this study, the Box–Behnken Design (BBD) was adopted to optimize the ultrasound-assisted extraction (UAE) conditions of polyphenols from Hericium erinaceus (H. erinaceus) on the basis of single-factor experiments, with extraction time, solid–liquid ratio and ethanol concentration as the key investigation [...] Read more.
In this study, the Box–Behnken Design (BBD) was adopted to optimize the ultrasound-assisted extraction (UAE) conditions of polyphenols from Hericium erinaceus (H. erinaceus) on the basis of single-factor experiments, with extraction time, solid–liquid ratio and ethanol concentration as the key investigation factors. The optimal extraction parameters were determined as follows: extraction time of 56.85 min, solid–liquid ratio of 1:56.71 g/mL and ethanol concentration of 44.64%, under which the actual yield of the total polyphenol crude extract (TPCE) reached 0.9985 ± 0.03%, which was highly consistent with the theoretical predicted value of 0.9960%, verifying the good fitting degree of the established model. Taking L-ascorbic acid as the positive control, the antioxidant activity of TPCE was evaluated by determining its scavenging capacity against ABTS·+, ·OH and DPPH· free radicals, and the half-maximal effective concentration (EC50) values were measured to be 0.8850, 0.9490 and 4.198 mg/mL, respectively. With acarbose as the reference drug, the inhibitory effects of TPCE on α-amylase and α-glucosidase related to carbohydrate metabolism were assayed, and the corresponding half-maximal inhibitory concentration (IC50) values were 0.0135 and 130.3 mg/mL, respectively. Furthermore, ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) was employed for the tentative identification of bioactive components in TPCE, and a total of 48 and 64 chemical constituents were characterized in negative and positive ion modes, respectively, providing a chemical basis for the biological activities of TPCE. This study confirmed that UAE is an efficient and feasible technology for extracting polyphenols from H. erinaceus, which lays a theoretical foundation for the development and utilization of its polyphenols, and also provides novel insights into the development of natural functional ingredients and potential therapeutic agents for the intervention of type 2 diabetes. Additionally, the findings further validate edible fungi as a valuable reservoir of natural bioactive substances, with promising application prospects in the research and development of functional foods and pharmaceuticals targeting metabolic diseases. Full article
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Article
Research on Control Factors and Parameter Optimization of Surfactant Flooding in Low-Permeability Reservoirs Using Random Forest Algorithm
by Yangnan Shangguan, Chunning Gao, Junhong Jia, Jinghua Wang, Guowei Yuan, Huilin Wang, Jiangping Wu, Ke Wu, Yun Bai, Hengye Liu and Yujie Bai
Processes 2026, 14(7), 1108; https://doi.org/10.3390/pr14071108 - 29 Mar 2026
Viewed by 345
Abstract
As oil and gas development increasingly targets low and ultra-low permeability reservoirs, conventional recovery techniques often prove insufficient for mobilizing residual oil. Surfactant flooding, a key chemical enhanced oil recovery (EOR) technology, thus requires careful system optimization and mechanistic investigation. This study focuses [...] Read more.
As oil and gas development increasingly targets low and ultra-low permeability reservoirs, conventional recovery techniques often prove insufficient for mobilizing residual oil. Surfactant flooding, a key chemical enhanced oil recovery (EOR) technology, thus requires careful system optimization and mechanistic investigation. This study focuses on low-permeability reservoirs in the Changqing Oilfield, evaluating three surfactant systems—YHS-Z1 (a 7:3 mass ratio blend of hydroxypropyl sulfobetaine and cocamide), YHS-Z2 (a polyether carboxylate, a nonionic-anionic composite) and a middle-phase microemulsion system (Heavy alkylbenzene sulfonate and hydroxysulfobetaine were combined with a mass ratio of 7:3)—through a series of experiments including interfacial tension measurement, contact angle analysis, static and dynamic oil displacement tests, as well as emulsion transport/retention index assessments, to comprehensively characterize their oil displacement properties. Based on the experimental data, this study constructed four classical regression models: Ridge Regression, Random Forest (RF), Gradient Boosting Regression (GBR), and Support Vector Regression (SVR), and conducted a comparative analysis of their predictive performance. The results demonstrate that the Random Forest (RF) model achieved the optimal prediction performance, with a Mean Absolute Error (MAE) of 1.8245, a Mean Absolute Percentage Error (MAPE) of 4.78%, and a coefficient of determination (R2) of 0.9428 on the training set. Further analysis using the SHapley Additive exPlanations (SHAP) algorithm revealed that the retention index is the primary global factor (accounting for 49.79% of the variance), while significant intergroup differences exist in the primary factors across different surfactant systems. Concurrently, single-factor and multi-factor sensitivity analyses were conducted to elucidate synergistic effects and threshold behaviors among parameters. The optimal parameter combination, identified via a random search method, achieved a predicted recovery factor of 45.61%, representing a 6.57% improvement over the highest experimental value. This study demonstrates that machine learning methods can effectively identify the dominant factors in oil displacement and enable synergistic parameter optimization, thereby providing a theoretical foundation for the efficient development of surfactant flooding in low-permeability reservoirs. Full article
(This article belongs to the Topic Enhanced Oil Recovery Technologies, 4th Edition)
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