jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cong Wang | Yufeng Fu | R. Wan | Le Zhao | Hongbo Wang | J. Guo | Qiang Liu | Shan Li | Shengtao Ma | Zhicai Wang | Wei Huang | Huimin Liu | Song Yang | Cong Nie
Source: Frontiers in Plant Science (article, 2025-08-20)
Field: Chemistry — Analytical Chemistry

Using preprocessed datasets to construct and interpret multiclass identification models This study expands the applicability of image and NIR spectroscopic data, offering researchers an effective methodology for investigating factors crucial to the quality control and improvement of agricultural products. Overfitting ; Interpretability ; Computer science ; Robustness (evolution) ; Artificial intelligence ; Data mining ; Support vector machine ; Pattern recognition (psychology) ; Machine learning ; Multiple kernel learning ; Kernel (algebra) ; Kernel method ; Mathematics ; Artificial neural network Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Arun Kumar Sethu | Gurdyal S. Besra | Sarah M. Batt | V. Chandrashekar | Manisha Singh | H. S. Akshatha | Meduri Bhagyalalitha | Karthik G. Pujar | Durgesh Paresh Bidye | Madhukumari | Gurubasavaraj V. Pujar
Source: RSC Medicinal Chemistry (article, 2025-12-10)
Field: Chemistry — Organic Chemistry

Developing pyrazole–oxadiazole conjugates as potent anti-tubercular agents: an <i>in silico</i> and <i>in vitro</i> approach Decaprenylphosphoryl-beta-d-ribose 2'-oxidase (DprE1) is a unique enzyme in <i>Mycobacterium tuberculosis</i> (Mtb) essential for the synthesis of the cell wall constituent arabinan. It converts decaprenyl phosphoryl ribose (DPR) to decaprenyl phosphoryl arabinose (DPA) with DprE2. Inhibition of DprE1 affects the integrity of the cellular membrane, causing cell wall rupture, leakage of cellular contents, and ultimately, cell death. Structural analysis of the DprE1 binding site reveals three binding regions: hydrophobic head region, planar core nucleus, and solvent-accessible tail region. Based on these structural features and reported inhibitors, structure-based drug design (SBDD) was employed to design and develop 22 novel pyrazole-oxadiazole conjugates (PO1-PO22), combining a hydrophobic pyrazole and planar oxadiazole connected to an amide linker. The designed compounds were synthesized and evaluated for antitubercular activity against the Mtb H37Rv strain using the Microplate Alamar Blue (MAB) assay. Among them, PO3 and PO4 exhibited potent activity with MIC values of 0.24 and 0.53 μM, respectively, better than the known DprE1 inhibitor TCA-1 (MIC 0.66 μM). Six compounds with MICs < 2.5 μM were further screened for DprE1 inhibition. PO3 and PO4 showed IC<sub>50</sub> values of 32.7 ± 6.0 and 39.2 ± 7.3 μM, suggesting DprE1 may not be their primary target. Molecular dynamics simulations (200 ns) supported the limited stability of DprE1-ligand complexes. Notably, the active compounds displayed excellent safety (IC<sub>50</sub> > 500 μM) in NIH/3T3 fibroblast cytotoxicity assays. Overall, the pyrazole-oxadiazole conjugates represent promising anti-Mtb agents, likely acting through multi-target mechanisms within the pathogen. Cytotoxicity ; Conjugate ; Fibroblast ; Cell culture ; Enzyme ; Cell ; Structure–activity relationship ; Amide ; Lysis Carbohydrate Chemistry and Synthesis ; Calcium signaling and nucleotide metabolism ; Tuberculosis Research and Epidemiology
Authors: Shuo Xu | Hang Su | Lei Zhao
Source: Applied Sciences (article, 2025-10-11)
Field: Chemistry — Organic Chemistry

Research on the Classification Method of Pinus Species Based on Generative Adversarial Networks and Convolutional Neural Networks With the rapid expansion of the global timber trade, accurate wood identification has become essential for regulating ecosystems and combating illegal logging. Traditional methods, largely reliant on manual analysis, are inadequate for large-scale, high-precision demands. A multi-architecture fusion network model that combines generative adversarial networks and one-dimensional convolutional neural networks aims to solve the problems in data quality and the challenges in classification accuracy existing in the classification process of pine tree species. The generative adversarial network is used to improve the data, which effectively expands the scale of the training set. Moreover, the one-dimensional convolutional neural network is utilized to extract local and global features from the spectral data, which improves the classification accuracy of the model and also makes the model more stable. The results obtained from the experiment show that MAFNet can achieve an accuracy rate of 99.63% in the classification of pine species. The model performed best on cross-sectional data. The research finds that MAFNet, relying on the strategy of integrating data enhancement and deep feature extraction, provides strong technical support for the rapid, accurate and non-destructive identification of pine species. Adversarial system ; Convolutional neural network ; Generative grammar ; Process (computing) ; Identification (biology) ; Generative adversarial network ; Artificial neural network ; Feature (linguistics) Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Image Processing and 3D Reconstruction
Authors: Abel Chernet Kabiso | Cheng-Hao Ko
Source: Scientific Reports (article, 2025-09-24)
Field: Chemistry — Analytical Chemistry

Attention-enhanced residual autoencoder for NIR spectral feature extraction and classification of grain varieties Accurate identification of grain cultivars is critical for improving crop yields, streamlining agricultural workflows, and ensuring global food security. Near-infrared (NIR) spectroscopy offers a rapid, non-destructive solution for grain classification. However, its effectiveness hinges on extracting meaningful spectral features. We propose SpecFuseNet, an attention-enhanced residual autoencoder, as a lightweight deep learning model for extracting NIR spectral features and classifying grain varieties. The encoder integrates Fused Efficient Channel Attention (FusedECA) and a Spectral Residual Gate (SRG) to extract informative spectral features, while a mirrored decoder enables robust spectral reconstruction. This architecture supports both spectral reconstruction and cultivar classification, with robust performance and minimal complexity. We evaluated SpecFuseNet on three NIR datasets: barley (1,200 samples, 24 varieties), chickpea (950 samples, 19 varieties), and sorghum (500 samples, 10 varieties) using stratified 5-fold cross-validation. The model achieved classification accuracies of 89.72%, 96.14%, and 90.67%, respectively, outperforming PCA-based machine learning models (SVM, Random Forest, XGBoost) and deep learning baselines such as standard Autoencoder (AE) and Convolutional Sparse Autoencoder (CSAE). These results demonstrate SpecFuseNet's potential as a fast, interpretable, and deployable solution for real-time classification in field-based and resource-limited settings, with a lightweight design that enables deployment on portable or smartphone-connected NIR spectrometers, supporting sustainable and precise agricultural practices. Autoencoder ; Residual ; Pattern recognition (psychology) ; Deep learning ; Encoder ; Feature extraction ; Near-infrared spectroscopy Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Rashidah Ghazali | Herlina Abdul Rahim | ⁠Syahidah Nurani Zulkifli
Source: Indonesian Journal of Electrical Engineering and Computer Science (article, 2025-09-01)
Field: Chemistry — Analytical Chemistry

Prediction of broiler shear force using near infrared spectroscopy with second derivative linear modeling This study explores the use of linear predictive models, specifically principal component regression (PCR) and partial least squares (PLS), in combination with a cost-effective near infrared spectroscopy (NIRS) system to noninvasively assess the texture of raw broiler meat. The findings demonstrate that appropriate pre-processing techniques, such as excluding the visible spectrum and applying the second-order Savitzky-Golay (SG) derivative with an optimal filter length (FL), enhance model performance. Notably, the PLS model outperformed PCR, requiring fewer latent variables (LVs) to achieve accurate predictions. This suggests that PLS more effectively captures key spectral features associated with meat texture, making it a promising approach for assessing raw broiler meat quality in a practical, cost-efficient, and non-invasive manner. These results highlight the potential of integrating linear predictive models with NIRS technology for reliable texture analysis in the poultry industry. Broiler ; Partial least squares regression ; Principal component analysis ; Near-infrared spectroscopy ; Second derivative ; Linear regression ; Principal component regression ; Linear model ; Texture (cosmology) Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Animal Nutrition and Physiology
Authors: Alessandra Napolitano
Source:  (article, 2025-09-16)
Field: Chemistry — Organic Chemistry

Abstracts of the 2nd International Electronic Conference on Antioxidants (IECAN 2025), 7–9 April 2025 The 2nd International Electronic Conference on Antioxidants (IECAN 2025) was organized by MDPI and MDPI Antioxidants. The conference took place online from 7–9 April 2025. This conference report is an abstract collection from six different sessions of IECAN 2025. Electronic materials ; Committee on Space Research ; MEDLINE ; Electronic library Free Radicals and Antioxidants ; Advanced Chemical Sensor Technologies ; bioluminescence and chemiluminescence research
Authors: Zhengdao Chen
Source: Modern Physical Chemistry Research (article, 2025-01-01)
Field: Materials Science — General Materials Science

Evaluation of the removal efficiency of perfluorinated compounds in the drinking water treatment process The research on the removal mechanism and risk control of perfluorinated compounds (PFCs) in drinking water treatment presents complex characteristics of interdisciplinary integration. This paper systematically analyzed the environmental fate behavior of PFCs, revealing the efficiency boundaries and molecular-scale action mechanisms of multi-stage processes such as coagulation - adsorption - membrane separation. It was found that hydrophobic adsorption dominated the removal of long-chain PFCs (Qmax=689 mg/g), while short-chain homologous substances relied more on the bond-breaking degradation of advanced oxidation (k=0.12 min-1). Innovatively proposed a collaborative optimization path for the functional design of MOFs materials and electrocatalytic membrane reactors, combined with artificial intelligence-driven digital twin technology, achieving a 41% reduction in energy consumption with a removal rate of over 97%. The research provides a full-chain solution from molecular mechanisms to engineering applications for the risk management and control of PFCs. Process (computing) ; Environmental chemistry ; Chemistry ; Environmental science ; Process engineering ; Computer science ; Engineering Material Properties and Applications
Authors: O.V. Myronyuk | D.V. Baklan | D.O. Savchenko | E.A. Pashchenko
Source: Scientific notes of Taurida National V I Vernadsky University Series Technical Sciences (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

TUNING THE WETTING PROPERTIES OF LOW-DENSITY POLYETHYLENE FILMS VIA CHEMICAL OXIDATION AND STEARIC ACID MODIFICATION Impregnated surfaces have recently become a promising class of materials that overcome the limitations of classic superhydrophobic coatings and demonstrate high stability and multifunctionality in such areasof application as anti-icing, water-repellent coatings, and barrier coatings.The problem of controlling the wettability of polyethylene as a typical polyolefin substrate is considered with the aim of developing a reproducible and environmentally compatible method for regulating its surface energy.A combined strategy was applied, which included the fabrication of a template using a femtosecond laser, thermo-pressing of polyethylene to obtain textured replicas, followed by chemical oxidation with nitric acid and potassium permanganate, and final hydrophobization with stearic acid as a biologically derived alternative to fluorinated modifiers.Surface morphology, oxidation effects, and wetting behavior were investigated using microscopy and contact angle measurements.It was shown that pure polyethylene has a water contact angle of ~91, which corresponds to its nonpolar and hydrophobic nature.Oxidation significantly reduced the contact angle to 28 due to surface polarization and possible deposition of manganese oxides, while further treatment with stearic acid restored and even exceeded the initial values, yielding ~96 on flat surfaces.The results showed that texturing increased the contact angle to 135, and in combination with oxidation and hydrophobization to 145, which is 54 higher than the untreated material.It was shown that the modified surfaces were resistant to wetting by liquids with low surface tension, with a critical surface energy of 55 mJ/m2, which is considered a high threshold for non-wettability.It has been established that the key mechanism of stable hydrophobization is the fixation of stearic acid molecules by polar groups introduced during oxidation, with the external orientation of hydrocarbon tails.The approach has been found to be reproducible and does not compromise the mechanical integrity of polyethylene.Prospects for further improvement include the introduction of metal nanoparticles to form secondary textures and their subsequent hydrophobization, which may contribute to the development of superhydrophobic and infused surfaces.The results highlight a scalable methodology to produce non-fluorinated, durable, and superhydrophobic polymer surfaces. Stearic acid ; Wetting ; Polyethylene ; Chemical modification ; Surface modification Surface Modification and Superhydrophobicity ; Polymer Science and Applications ; Polymer composites and self-healing
Authors: Jiahao Liu | Zhifang Yang | Guangying Cui | Junshuai Lv | Xiang Yu
Source: Lubricants (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Toughening Mechanisms of Diamond-like Carbon Films via Non-Carbide Metal Doping The inherent brittleness and poor fracture toughness of diamond-like carbon (DLC) films significantly limit their long-term reliability in mechanical and tribological applications. Among various strategies to enhance toughness, doping with non-carbide-forming metals (e.g., Ag, Cu) has emerged as a highly effective approach due to their ductile properties and compatibility with carbon matrices. This review comprehensively examines the underlying toughening mechanisms induced by non-carbide metal doping in DLC films. We systematically analyze how metal incorporation influences film microstructure, stress state, and crack behavior throughout the entire lifecycle—from deposition to mechanical testing. Five primary toughening mechanisms are identified and discussed: (I) bombardment-induced compressive stress relaxation during film growth; (II) refinement of carbon atomic clusters and enhancement of grain boundary sliding; (III) inhibition of dislocation accumulation through moderated carbon atom repulsion; (IV) plastic deformation, crack bridging, and strain field relaxation at crack tips; (V) shear-induced stress relief via soft metal particles. Among these, Mechanism IV (ductile phase toughening) is identified as the dominant contributor, and their synergistic action can lead to orders of magnitude improvement in wear resistance and a significant increase in crack propagation resistance. Furthermore, the critical role of doping content is emphasized, revealing an optimal concentration range (e.g., ~10–15 at.% for Ag and Cu) beyond which toughness may deteriorate due to excessive boundary formation or hardness loss. This work provides a mechanistic framework for designing toughened DLC films and guides future efforts in developing high-performance, durable carbon-based coatings. Brittleness ; Toughness ; Fracture toughness ; Toughening ; Doping ; Carbon fibers ; Grain boundary ; Fracture mechanics ; Residual stress ; Stress relaxation Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Boron and Carbon Nanomaterials Research
Authors: Hayato Yamada | Ryuto Kojo | Takafumi Shiraogawa | Daisuke Shimizu | Daiki Imoto | Nhat Le | Ayato Sato | Atsushi Usami | Jun‐ya Hasegawa | Lawrence T. Scott | Kazuma Amaike | Kenichiro Itami
Source: ChemRxiv (article, 2025-09-03)
Field: Materials Science — Biomaterials

A bioactive nanocarbon that targets cross-species protein–protein interactions The quest for bioactive molecules has long been confined to a rather narrow chemical space, limiting the discovery of uncharted chemical structures. Identifying such compounds is essential for uncovering previously unrecognized mechanisms of action and addressing limitations in conventional drug discovery. Here we unveil molecular nanocarbons, nanometer-scale, rigid, π-extended, and carbon-rich/hydrophobic frameworks widely studied in materials science, as a previously untapped class of bioactive agents capable of modulating protein–protein interactions (PPIs). We discovered a molecular nanocarbon that inhibits structurally conserved ligand–receptor pairs in insect diapause signaling (the ligand PND and its receptor PND-2) and human immune signaling (IL-17A and its receptor IL-17RA). The discovered planar nanocarbon compound, named clionene, disrupts both interactions, outperforming the flavonoid cyanidin in potency and selectivity. Structural modeling and molecular dynamics simulations reveal that clionene binds to a hydrophobic pocket at the IL-17A/IL-17RA interface via shape complementarity and π-interactions. These findings establish molecular nanocarbons as bioactive modulators of conserved PPIs, expanding the accessible chemical space for drug discovery and opening unconventional directions in cross-kingdom therapeutic targeting. Protein–protein interaction ; Chemistry ; Business ; Nanotechnology ; Computational biology ; Biochemistry ; Biology ; Materials science Nanoparticle-Based Drug Delivery
Authors: Abhinab Borah
Source: The Asian Review of Civil Engineering (article, 2025-11-10)
Field: Materials Science — Polymers and Plastics

Mechanical Performance and Engineering Applications of Waste Plastic–Derived Materials: A Structured Review The increasing generation of plastic waste presents significant environmental challenges while simultaneously creating opportunities for the development of sustainable engineering materials. In recent years, waste-plastic–derived materials have gained considerable attention as potential alternatives to conventional polymers and composites in engineering applications. This paper presents a critical review of the mechanical performance and industrial applicability of materials derived from waste plastics, with particular emphasis on engineering systems. A systematic qualitative review methodology is adopted, encompassing peer-reviewed literature on mechanically recycled polymers, plastic-based composites, and plastic-modified construction materials. Key mechanical properties-including tensile, compressive, flexural, impact, fatigue, and creep behavior-are examined, along with the influence of recycling processes, material composition, and reinforcement strategies on performance. The reviewed studies indicate that unreinforced recycled plastics generally exhibit reduced mechanical properties compared to virgin polymers due to degradation during reprocessing. However, significant performance improvements are consistently reported through fiber reinforcement, hybrid composite design, nanofiller incorporation, and optimized processing techniques. The applicability of waste-plastic–derived materials in the construction, automotive, consumer goods, energy, and public utility sectors is discussed in relation to technical, economic, and regulatory considerations. In addition, key research directions emphasizing advanced recycling technologies, material optimization, standardization, and circular-economy integration are identified. The findings establish performance trends, design strategies, and application boundaries for waste-plastic–derived materials in engineering systems. Plastic waste ; Materials processing ; Creep ; Sustainability ; Key (lock) ; Material efficiency ; Plastics industry Natural Fiber Reinforced Composites ; Microplastics and Plastic Pollution ; Polymer crystallization and properties
Authors: Sonia Soltani
Source: JOURNAL of QASSIM UNIVERSITY FOR SCIENCE (article, 2025-10-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Energy-Efficient Cooling with Large Magnetocaloric Effect in Dy<sub>2</sub>In<sub>0.8</sub>Al<sub>0.2</sub> and Dy<sub>2</sub>In<sub>0.7</sub>Al<sub>0.3</sub> ingots In this paper, the magnetocaloric effect in both second- and first-order magnetic phase transitions for Dy2In0.8Al0.2 and Dy2In0.7Al0.3 ingots, respectively, was simulated using the mean-field model. This model investigated the exchange mean-field by analyzing isothermal magnetization data. Through a scaling technique and using the Brillouin function key magnetic parameters such as the saturation magnetization M0, the total magnetic moment quantum number J, and the Lande factor g for each compound were effectively determined. A strong correlation between simulated and experimental magnetization values was established. The magnetic entropy change formula was also derived, and entropy change curves under various magnetic fields around the magnetic phase transition were generated. Under a 7 T magnetic field, the relative cooling power (RCP) is 487.6 J•kg -1 for the Dy 2 In 0.8 Al 0.2 and 594.3 J•kg -1 for the Dy 2 In 0.7 Al 0.3 sample. The corresponding maximum magnetic entropy change (|∆S M Max |) values are 10.48 J•kg -1 •K -1 and 12.22 J•kg -1 •K -1 , respectively. These findings highlight the potential of these materials for energy-efficient applications in environmentally sustainable magnetic refrigeration systems. Magnetic refrigeration ; Brillouin and Langevin functions ; Magnetization ; Magnetic field ; Magnetic moment ; Scaling ; Saturation (graph theory) ; Entropy (arrow of time) Magnetic and transport properties of perovskites and related materials
Authors: 号东 陈
Source: Material Sciences (article, 2025-01-01)
Field: Materials Science — Biomaterials

Study on Design and Application of Peptide Hydrogels Based on the &lt;i&gt;β&lt;/i&gt;-Sheet Structure 肽基水凝胶作为生物活性材料在药学、纳米材料化学、组织工程等领域受到广泛关注。肽水凝胶的设计往往遵循自下而上的思路，即基于分子自组装的方法，以微观结构为基础扩展到宏观结构的形成。这种从微观角度出发的方法缺乏具体的模型阐述，初学者往往难以上手。因此，我们根据以β-折叠为基本自组装结构的肽基水凝胶的研究，总结了氨基酸对于肽自组装行为以及凝胶结构与应用的影响，期望可以帮助研究者在设计多肽序列时可以尽快地找到切入点，开发出具有特殊功能的肽基水凝胶材料。As bioactive materials, peptide-based hydrogels have attracted extensive attention in the fields of pharmacy, nanomaterial chemistry, tissue engineering, etc. The design of hydrogels often follows the bottom-up approach, that is the method based on molecular self-assembly, extending from microstructure to the formation of macrostructure. But this method from the micro-perspective lacks specific model elaboration, making it difficult to get started for beginners. Therefore, based on some studies on peptide-based hydrogels with β-sheet as the basic self-assembly structure, we summarized the influence of different amino acid sequences on peptide self-assembly behavior, and the structure and application of gel, hoping to help researchers find the entry point as soon as possible when designing peptide sequences, and design and develop peptide-based hydrogel materials with special functions. Materials science Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis