jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Asif Ali T S | Aiswarya Velekat Santhosh | M. Venkatesh
Source: European Journal of Nutrition & Food Safety (article, 2025-01-09)
Field: Materials Science — Biomaterials

Effect of Chitosan-based Edible Films on the Sensory and Microbial Quality of Ready-to-Eat Paneer during Storage Aims: The aim of the study is to find the effect of chitosan-based edible films on the sensory and microbiological characteristics of ready-to-eat paneer during storage at ambient and refrigeration temperature. Study Design: This study was experimental and conducted in a lab. Place and Duration of Study: The study was conducted at the Department of Dairy Technology, Dairy Science College, Hebbal, Bengaluru, Karnataka, India, between October 2022 and July 2023. Methodology: The ready to eat paneer prepared with 5 % fructooligosaccharides, 40 % sugar and 15 % orange juice will be aseptically wrapped with chitosan based edible film made of 1.5% chitosan and 0.75% glycerol. The product will be analysed at both ambient temperature (27±1°C) with 1days interval and refrigeration temperature (4±1°C) with 3 days interval. Sensory analysis by 9-point hedonic scale and microbial analysis of standard plate count, coliforms count, yeast and mold count were assessed. ANOVA was the method used for statistical analysis, and the significance between the control and sample was assessed using a critical difference (CD) at a 5% significance level. Results: The sensory attributes ratings of the control paneer and edible chitosan film packed ready to eat paneer samples are non-significant (P&gt;0.05) to each other and decreased consistently throughout storage at both conditions. Throughout storage, the coliform count was undetectable. The initial yeast and mold count of the chitosan film-packed ready-to-eat paneer was 0.48 log₁₀ cfu/g, and the standard plate count was 2.30 log₁₀ cfu/g. Both counts increased during storage but remained significantly (P&lt;0.05) lower than those in the control paneer. Conclusion: The storage studies revealed that control paneer can be stored for 1 days at ambient temperature and 6 days at refrigeration temperature without any spoilage and chitosan film packed ready to eat paneer can be stored for 2 days at ambient temperature and 9 days at refrigeration temperature without any spoilage. Food science ; Total Viable Count ; Plate count ; Mold ; Sensory analysis ; Chitosan ; Salmonella ; Sugar ; Mathematics ; Chemistry ; Biology ; Botany Nanocomposite Films for Food Packaging ; Coconut Research and Applications ; Phytochemicals and Antioxidant Activities
Authors: Chen Wang | Xiao‐Nan Li | Zijuan Zhang | Xuan Luo | Jianrong Cai | Aichen Wang
Source: Agriculture (article, 2025-10-18)
Field: Chemistry — Analytical Chemistry

Nondestructive Quality Detection of Characteristic Fruits Based on Vis/NIR Spectroscopy: Principles, Systems, and Applications Nondestructive quality detection of characteristic fruits is essential for ensuring nutritional value, economic viability, and consumer safety in global supply chains, yet traditional destructive methods compromise sample integrity and scalability. Visible and near-infrared (Vis/NIR) spectroscopy offers a transformative solution by enabling rapid, non-invasive multi-attribute quantification through molecular overtone vibrations. This review examines recent advancements in Vis/NIR-based fruit quality detection, encompassing fundamental principles, system configurations, and detection strategies calibrated to fruit biophysical properties. Firstly, optical mechanisms and system architectures (portable, online, vehicle-mounted) are compared, emphasizing their compatibility with fruit structural complexity. Then, critical challenges arising from fruit-specific characteristics—such as rind thickness, pit interference, and spatial heterogeneity—are analyzed, highlighting their impact on spectral accuracy. Applications across diverse fruit categories (pitted, thin-rinded, and thick-rinded) are systematically reviewed, with case studies demonstrating the robust prediction of key quality indices. Subsequently, considerations in model development and validation are presented. Finally, persistent limitations in model transferability and environmental adaptability are discussed, proposing future research directions centered on integrating hyperspectral imaging, AI-driven calibration transfer, standardized spectral databases, and miniaturized, field-deployable sensors. Collectively, these methodological breakthroughs will pave the way for autonomous, next-generation quality assessment platforms, revolutionizing postharvest management for characteristic fruits. Hyperspectral imaging ; Adaptability ; Transferability ; Quality (philosophy) ; Quality assurance ; Traceability ; Rigour ; Quality assessment Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Qi Ren | Jiandong Fang | Yudong Zhao
Source: Applied Sciences (article, 2025-05-29)
Field: Materials Science — Polymers and Plastics

Prediction Method of Tangerine Peel Drying Moisture Ratio Based on KAN-BiLSTM and Multimodal Feature Fusion Tangerine peel, rich in moisture (75–90%) and medicinal value, requires drying to prevent spoilage and extend shelf life. Traditional heat pump drying often causes uneven airflow, leading to inconsistent drying and nutrient loss, compromising product quality and storage stability. In this study, a prediction model of drying moisture ratio of tangerine peel based on Kolmogorov–Arnold network bidirectional long short-term memory (KAN-BiLSTM) and multimodal feature fusion is proposed. A pre-trained visual geometry group U-shaped network (VGG-UNet) is employed to segment tangerine peel images and extract color, contour, and texture features, while airflow distribution is simulated using finite element analysis (FEA) to obtain spatial location information. These multimodal features are fused and input into a KAN-BiLSTM model, where the KAN layer enhances nonlinear feature representation and a multi-head attention (MHA) mechanism highlights critical temporal and spatial features to improve prediction accuracy. Experimental validation was conducted on a dataset comprising 432 tangerine peel samples collected across six drying batches over a 480 min period, with image acquisition and mass measurement performed every 20 min. The results showed that the pre-trained VGG-UNet achieved a mean intersection over union (MIoU) of 93.58%, outperforming the untrained model by 9.41%. Incorporating spatial features improved the coefficient of determination (R2) of the time series model by 0.08 ± 0.04. The proposed KAN-BiLSTM model achieved a mean absolute error (MAE) of 0.024 and R2 of 0.9908, significantly surpassing baseline models such as BiLSTM (R2 = 0.9049, MAE = 0.0476) and LSTM (R2 = 0.8306, MAE = 0.0766), demonstrating superior performance in moisture ratio prediction. Materials science Textile materials and evaluations ; Infrared Thermography in Medicine ; Remote Sensing and Land Use
Authors: Chenming Cheng | Jin Lei | Zheng Zhu | Lijian Lu | Zhi Wang | Junwu Tao | Xinyan Qin
Source: Frontiers in Plant Science (article, 2025-04-07)
Field: Chemistry — Analytical Chemistry

A novel multiscale feature enhancement network using learnable density map for red clustered pepper yield estimation The model achieved superior accuracy in RCP yield estimation by using LDM in conjunction with MFEN for joint training. Firstly, the integration of LDM significantly improved the accuracy of the model, with a 0.98% improvement over the previous iteration. Compared to other feature extraction networks, MFEN had the lowest mean absolute error (MAE) of 5.42, root mean square error (RMSE) of 10.37 and symmetric mean absolute percentage error (SMAPE) of 11.64%. It also achieved the highest R-squared (R²) value of 0.9802 on the test dataset, beating the best performing DSNet by 0.98%. Notably, despite its multi-column structure, the model has a significant advantage in terms of parameters, with only 13.08M parameters (a reduction of 3.18M compared to the classic single-column network CSRNet). This highlights the model's ability to achieve the highest accuracy while maintaining efficient deployment capabilities. The proposed method provides an robust algorithmic support for efficient and intelligent yield estimation in RCP. Pepper ; Yield (engineering) ; Feature (linguistics) ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Mathematics ; Biology ; Horticulture ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Samuel Pinilla | Ana Sanabria | Jia Bi | Karen Egiazarian
Source: Research Square (preprint, 2025-08-04)
Field: Materials Science — Materials Chemistry

What Makes Neural Networks Trainable? Invexity as a Structural Design Principle in AI Computer science ; Probabilistic logic ; Artificial neural network ; Maxima and minima ; Artificial intelligence ; Mathematical optimization ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Brain Tumor Detection and Classification ; Stochastic Gradient Optimization Techniques
Authors: Yourui Huang | Jiale Pang | Shuaishuai Yu | Jing Su | Shuainan Hou | Tao Han
Source: Agriculture (article, 2025-06-15)
Field: Chemistry — Analytical Chemistry

Reconstruction, Segmentation and Phenotypic Feature Extraction of Oilseed Rape Point Cloud Combining 3D Gaussian Splatting and CKG-PointNet++ Phenotypic traits and phenotypic extraction at the seedling stage of oilseed rape play a crucial role in assessing oilseed rape growth, breeding new varieties and estimating yield. Manual phenotyping not only consumes a lot of labor and time costs, but even the measurement process can cause structural damage to oilseed rape plants. Existing crop phenotype acquisition methods have limitations in terms of throughput and accuracy, which are difficult to meet the demands of phenotype analysis. We propose an oilseed rape segmentation and phenotyping measurement method based on 3D Gaussian splatting with improved PointNet++. The CKG-PointNet++ network is designed to integrate CGLU and FastKAN convolutional modules in the SA layer, and introduce MogaBlock and a self-attention mechanism in the FP layer to enhance local and global feature extraction. Experiments show that the method achieves a 97.70% overall accuracy (OA) and 96.01% mean intersection over union (mIoU) on the oilseed rape point cloud segmentation task. The extracted phenotypic parameters were highly correlated with manual measurements, with leaf length and width, leaf area and leaf inclination R2 of 0.9843, 0.9632, 0.9806 and 0.8890, and RMSE of 0.1621 cm, 0.1546 cm, 0.6892 cm2 and 2.1144°, respectively. This technique provides a feasible solution for high-throughput and rapid measurement of seedling phenotypes in oilseed rape. Point cloud ; Segmentation ; Computer science ; Feature (linguistics) ; Artificial intelligence ; Feature extraction ; Pattern recognition (psychology) ; Linguistics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Anna Martin | Damian Łukawski | D Biernacka | Karolina Z. Milowska | Agnieszka Łękawa-Raus | Alina Dudkowiak
Source: Cellulose (article, 2025-10-10)
Field: Materials Science — Polymers and Plastics

Sodium carboxymethylcellulose/carbon nanotube composite coatings: a sustainable approach to water detection This study explores the development of sodium carboxymethylcellulose/carbon nanotube composite coatings for sustainable water sensors. Sodium carboxymethylcellulose (NaCMC), a biodegradable biopolymer, was used as a dispersing matrix for carbon nanotubes (CNT), forming a stable conductive composite. Resistance was analysed in a CNT concentration range of 5–95 wt%, with a percolation threshold at ~ 9.14 wt%. The water interaction was examined under high humidity, droplet deposition, and in full immersion. Coatings with a low CNT content exhibited significant resistance changes, while higher CNT concentrations (> 50 wt%) provided greater stability. Mechanical durability was assessed in abrasion and bending tests, revealing high structural integrity. After 35 cycles of abrasion, the coating resistance increased by ~ 65%, while bending resulted in minor resistance variations, below 1% for inward bending and less than 3% for outward bending. In addition to experiments, semi-empirical and atomistic simulations have revealed that NaCMC hinders the electron transport in individual CNT, increasing the coating resistance. However, even a small amount of water can screen this effect. The presence of NaCMC on CNT junctions, which play a crucial role in charge transport within CNT coatings, can enhance or reduce their transport properties, depending on the junction type. The calculations have also shown that NaCMC, because of its rigidity, binds weakly to CNT. These findings highlight NaCMC/CNT composites as promising materials for green electronics, including humidity sensors and water-sensitive conductive coatings. Carbon nanotube ; Coating ; Composite number ; Bending ; Nanotube ; Abrasion (mechanical) ; Nanocomposite ; Durability ; Percolation threshold Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Electrohydrodynamics and Fluid Dynamics
Authors: Gaurav Deshmukh | Pushkar Ghanekar | Jeffrey Greeley
Source: Journal of the Indian Institute of Science (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Deep Learning for Computational Heterogeneous Catalysis: Fundamentals and Applications Computer science ; Artificial intelligence Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Kazuhiro Takeda | Naoya Ohtsuka | Toshiyasu Suzuki | Norie Momiyama
Source: Artificial Intelligence Chemistry (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Machine learning-guided synthesis of prospective organic molecular materials: An algorithm with latent variables for understanding and predicting experimentally unobservable reactions Chemists have traditionally relied on heuristic approaches to qualitatively assess chemical structure–property relationships and interpret experimental outcomes. However, these methods are inherently limited in handling large volumes of data and integrating them effectively into experimental planning. Understanding the interrelationships among different substitution patterns of organic molecular materials is crucial for optimizing synthetic conditions and expanding their applicability. In this study, we developed a machine learning (ML) algorithm incorporating latent variables to predict unobservable reactions and synthetic conditions for organic materials, specifically perfluoro-iodinated naphthalene derivatives. The algorithm accurately estimated substitution pattern relationships and reaction yields, which were experimentally validated with high-yield outcomes. Our findings reveal that latent variables effectively capture underlying physicochemical relationships, achieving an R value >0.99. This approach establishes an ML-guided framework that complements heuristic decision-making in chemistry and optimizes synthetic processes for the target molecule in an extrapolative manner. Further applications of this algorithm will focus on synthetic design and physicochemical property prediction, particularly for catalyst discovery and organic semiconductor optimization. • ML algorithm predicts unobservable reactions using latent variables. • Latent variables correlate with NBO charges, rationalizing reaction mechanisms. • High prediction accuracy achieved with RMSE < 1% in extrapolation domain. • Experimental validation confirms accurate reaction yield predictions. • Potential applications in catalysts, semiconductors, and biosensors. Unobservable ; Latent variable ; Heuristic ; Extrapolation ; Synthetic data ; Property (philosophy) ; Focus (optics) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Nilam Qureshi | Jinhong Bang | Jaehyeok Doh
Source: Nanotechnology Reviews (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Deep learning in-depth analysis of crystal graph convolutional neural networks: A new era in materials discovery and its applications Abstract Materials informatics is increasingly essential for precise material property prediction, simplifying experimental processes. Traditional methods like density functional theory are sluggish and struggle with complex structure–property relationships. In contrast, artificial intelligence, especially crystal graph convolutional neural networks (CGCNN), excels in predicting material behaviors particularly in crystalline systems revolutionizing materials discovery and design. CGCNN leverages crystal lattice structures for accurate predictions; however, there remains a lack of systematic analysis addressing its foundational principles, performance boundaries, and areas for improvement. This article is novel in offering a comprehensive and critical evaluation of CGCNN, detailing its architecture, predictive strengths, limitations, and integration with emerging technologies such as generative models. It emphasizes benchmarking protocols, best practices, and forward-looking strategies to bridge traditional physics-based methods and modern data-driven approaches. By articulating both the achievements and current gaps in CGCNN-based materials modeling, providing valuable guidance for researchers aiming to harness deep learning for next-generation materials discovery. Convolutional neural network ; Deep learning ; Computer science ; Graph ; Artificial intelligence ; Data science ; Power graph analysis ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Feng Wang | Yuhang Tang | Ze-Bing Ma | Yucheng Jin | Jun Cheng
Source: Nature Communications (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Domain oriented universal machine learning potential enables fast exploration of chemical space of battery electrolytes Li-ion batteries, widely used in electronic devices, electric vehicles, and aviation, demand high energy density, fast charging capabilities, and broad operating temperature ranges. Computations combined with experiments have gained increasing attention for electrolyte development. However, the inherent complexity of electrolytes poses a significant challenge. Classical molecular dynamics often fails due to inaccuracies in force field parameters, while ab initio calculations are limited by high computational costs. Recently, machine learning molecular dynamics has emerged as an efficient and accurate alternative. However, its application is hindered by limited transferability of machine learning potentials. In this work, we developed a universal machine learning potential for electrolytes using an iterative training approach on randomly composed datasets, enabling the accurate computation of key properties for a broad range of electrolytes via molecular dynamics. Furthermore, coordination dynamics analysis of Li ion, by quantifying the coordination lifetime, provides a direct, quantitative measure of solvation strength. The universal machine learning potential for electrolytes facilitates the prediction and optimization of electrolyte properties, offering a powerful tool for electrolyte design. Electrolyte ; Solvation ; Battery (electricity) ; Field (mathematics) ; Molecular dynamics ; Computation ; Ab initio ; Transferability Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advanced Battery Technologies Research
Authors: Yujie Liu | Zhenyu Wang | Lingxiao Hang | 张国玉 Zhang Guoyu | XIAN Jiawei | Zhibin Gao | Sun Jun | SONG Haifeng | DING Xiangdong
Source: Acta Physica Sinica (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-driven elasticity prediction in advanced inorganic materials via convolutional neural networks &lt;sec&gt;Inorganic crystal materials have shown extensive application potential in many fields due to their excellent physical and chemical properties. Elastic properties, such as shear modulus and bulk modulus, play an important role in predicting the electrical conductivity, thermal conductivity and mechanical properties of materials. However, the traditional experimental measurement method has some problems such as high cost and low efficiency. With the development of computational methods, theoretical simulation has gradually become an effective alternative to experiments. In recent years, graph neural network-based machine learning methods have achieved remarkable results in predicting the elastic properties of inorganic crystal materials, especially, crystal graph convolutional neural networks (CGCNNs), which perform well in the prediction and expansion of material data.&lt;/sec&gt;&lt;sec&gt;In this study, two CGCNN models are trained by using the shear modulus and bulk modulus data of 10987 materials collected in the Matbench v0.1 dataset. These models show high accuracy and good generalization ability in predicting shear modulus and bulk modulus. The mean absolute error (MAE) is less than 13 and the coefficient of determination (&lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ R^2$\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;) is close to 1. Then, two datasets are screened for materials with a band gap between 0.1 and 3.0 eV and the compounds containing radioactive elements are excluded. The dataset consists of two parts: the first part is composed of 54359 crystal structures selected from the Materials Project database, which constitute the MPED dataset; the second part is the 26305 crystal structures discovered by Merchant et al. (&lt;ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://doi.org/10.1038/s41586-023-06735-9"&gt;2023 &lt;i&gt;Nature&lt;/i&gt; &lt;b&gt;624&lt;/b&gt; 80&lt;/ext-link&gt;) through deep learning and graph neural network methods, which constitute the NED dataset. Finally, the shear modulus and bulk modulus of 80664 inorganic crystals are predicted in this study This work enriches the existing material elastic data resources and provides more data support for material design. All the data presented in this paper are openly available at &lt;ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://doi.org/10.57760/sciencedb.j00213.00104"&gt;https://doi.org/10.57760/sciencedb.j00213.00104&lt;/ext-link&gt;.&lt;/sec&gt; Convolutional neural network ; Elasticity (physics) ; Artificial neural network ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Composite material Machine Learning in Materials Science