jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mbulelo Ngqase | Willie Nheta | Maje Phasha | Takalani Madzivhandila
Source: Preprints.org (preprint, 2025-06-27)
Field: Materials Science — Materials Chemistry

Effect of Casting Shakeout Temperature on Residual Stresses of Hypoeutectic High Chromium Iron Alloys Using Hole-Drilling Method Attention on residual stress development, magnitude, and distribution on abrasive wear materials, such as high chromium iron alloys, has been minimal, even though these materials are prone to tearing or inferior mechanical properties because of distortion during manufacturing processes, such as casting and heat treatment, leading to premature failure of cast components. Identification of the source and succeeding management of residual stress provides an opportune possibility to enhance product quality. In the current study, the as-cast hypoeutectic irons of ASTM A532, Type A, Class III, 25% Cr, i.e., sample A and sample B, were cast at 1384 and 1390 degrees Celsius and allowed to solidify and cool for approximately 1645 and 1295 minutes, respectively. Thus, the shakeout temperatures of sample A and sample B products were established to be approximately 60 and 180°C, respectively. Resultant residual stress and macrohardness were determined at ambient temperatures using the hole-drilling method and Brinell hardness tester machines, respectively. Higher hardness values of 600 BHN were established on sample B compared to 526 BHN for sample A. In addition, casting shakeout at higher temperatures, i.e., 180°C on sample B, yielded higher magnitudes of residual stress distribution compared to 60°C shakeout for sample A. Eutectic system ; Metallurgy ; Casting ; Chromium ; Materials science ; Drilling ; Residual ; Hole drilling method ; Alloy ; Computer science Metal Alloys Wear and Properties ; Welding Techniques and Residual Stresses ; Microstructure and Mechanical Properties of Steels
Authors: Ninja Begum | Manuj Kumar Hazarika
Source: Research in Agricultural Engineering (article, 2025-05-30)
Field: Chemistry — Analytical Chemistry

Spoilage detection of tomatoes using a convolutional neural network <br>With the increasing productivity in agriculture, it has become extremely essential to look for an advanced technique that will help to minimise losses. Recently, deep learning has outperformed the task of recognition and classification of fruits and vegetables automatically from images, finding applicability in this study. This work, thus, attempts to develop an automatic spoilage detection CNN model for tomatoes. In this work, a deep learning-based CNN model is trained and validated on a self-prepared dataset for classifying tomatoes as edible and spoilt is proposed. The dataset consisted of 810 images, out of which 572 images were considered for training and 238 images for validation. The model is also trained iteratively with varying epoch and batch sizes to evaluate the model in giving the highest classification accuracy. The highest accuracy of 99.70% was achieved at epoch 20 and batch size 32. Further evaluating the performance of the developed model using a confusion matrix, a precision, recall and accuracy of 100%, 87% and 95%, respectively, was obtained for the spoilage detection of tomatoes. Also, on establishing Pearson’s correlation between the predictive model and the sensory evaluation results, a Pearson correlation of 0.895 was obtained, showing that there is strong linear correlation between them. Convolutional neural network ; Food spoilage ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Hussein Abbas Khursheed | Furqan K. K. AL-Fatlawi | Hasan Salim Buktash
Source: Open Access Research Journal of Biology and Pharmacy (article, 2025-09-04)
Field: Chemistry — Physical and Theoretical Chemistry

High-pressure crystallography and its role in explaining polymorphism in (not related to living things) coordination complexes In solid-state chemistry, high-pressure crystallography (HPC) has become an essential method, especially for the investigation of inorganic coordination complexes. Properties like catalytic efficiency and magnetic behavior are impacted by polymorphism in these compounds. This systematic review summarizes recent developments, experimental methods, and the consequences of pressure-induced structural changes while assessing the contribution of HPC to the discovery and characterization of polymorphs. Polymorphism (computer science) ; Computational biology ; Genetics ; Biology ; Gene ; Allele Crystallography and molecular interactions ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: A. Abdi | Shidvash Vakilipour | Jafar Al-Zaili
Source: International Journal of Heat and Mass Transfer (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

Molecular dynamics simulation study on thermophysical properties of carbon nanotube-enhanced lithium fluoride as a high-temperature phase change material Lithium fluoride is combined with single-walled carbon nanotubes to enhance its performance as a phase change material for using the latent heat thermal energy storage approach in concentrated solar power systems. Molecular dynamics simulation using Large-scale Atomic/Molecular Massively Parallel Simulator is employed to evaluate thermophysical properties, including density, melting point, enthalpy, specific heat capacity, thermal conductivity, diffusion coefficients, and viscosity, across both solid and liquid phases. The addition of single-walled carbon nanotube increases the density by 3.11–6.35% in the system containing 704 carbon atoms and 5.47–10.26% in the system containing 1024 carbon atoms, while enhancing the thermal conductivity by 2.76–29.42% and 17.06–33.53% in the respective systems, thereby improving volumetric energy storage and heat transfer. A reduction in melting temperature and a minor enhancement in specific heat capacity, up to 2.6% at higher carbon concentration, are also observed. Diffusion coefficients are reduced by up to 33% and viscosity by up to 35% at higher SWCNT concentrations, demonstrating the material’s suitability for stationary thermal energy storage systems. Figure of merit analysis indicates that the composite phase change material with 1024 carbon atoms exhibits the best overall performance. These findings highlight the potential of single-walled carbon nanotube-enhanced lithium fluoride as a composite phase change material for thermal energy storage applications, validating the effectiveness of molecular dynamics simulations for high-temperature composite phase change material optimization in concentrated solar power systems. • A novel LiF–SWCNT composite PCM is developed with superior LHTES performance for CSP. • SWCNT presence enhances density, thermal conductivity, and specific heat capacity. • Melting temperature, enthalpy, and viscosity are reduced by the addition of SWCNT. • FoM analysis shows improved overall performance for higher carbon concentrations. Carbon nanotube ; Thermal energy storage ; Phase-change material ; Thermal conductivity ; Carbon fibers ; Molecular dynamics ; Latent heat ; Composite number ; Lithium (medication) Thermal Expansion and Ionic Conductivity ; Spacecraft and Cryogenic Technologies ; Phase Change Materials Research
Authors: Enrico Lorenzetti | Celia Municio-Diaz | Nicolas Minc | Arezki Boudaoud | Antoine Fruleux
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-06)
Field: Chemistry — Spectroscopy

Inferring diffusion, reaction, and exchange parameters from imperfect FRAP ABSTRACT Fluorescence recovery after photobleaching (FRAP) is broadly used to investigate the dynamics of molecules in cells and tissues, notably to quantify diffusion coefficients. FRAP is based on the spatiotemporal imaging of fluorescent molecules following an initial bleaching of fluorescence in a region of the sample. Although a large number of methods have been developed to infer kinetic parameters from experiments, it is still a challenge to fully characterize molecular dynamics from noisy experiments in which diffusion is coupled to other molecular processes or in which the initial bleaching profile is not perfectly controlled. To address this challenge, we have developed HiFRAP to quantify the reaction-(or exchange-)diffusion kinetic parameters from FRAP under imperfect experimental conditions. HiFRAP is based on a low-rank approximation of a kernel related to the model Green’s function and is implemented as an ImageJ/Python macro for (potentially curved) one-dimensional systems and for two-dimensional systems. To the best of our knowledge, HiFRAP offers features that have not been combined together: making no assumption on the initial bleaching profile, which does not need to be known; accounting for the limitation of the optical setup by diffraction; inferring several kinetic parameters from a single experiment; providing errors on parameter estimation; and testing model goodness. In the future, our approach could be applied to other dynamical processes described by linear partial differential equations, which could be useful beyond FRAP, in experiments where the concentration fields are monitored over space and time. SIGNIFICANCE Fluorescence recovery after photobleaching (FRAP) is a microscopy approach that is widely used to investigate the diffusion and transport of molecules in life sciences and in material sciences. Numerous methods have been developed to derive kinetic parameters such as diffusion and binding coefficients. However, these methods suffer from limitations associated with experimental constraints, such as technical noise or an imperfectly known initial condition. To circumvent these limitations, we developed a comprehensive approach to estimate several kinetic parameters from a single experiment, to assess the precision of estimation, and to test whether the underlying model is well-suited. We implemented this approach in HiFRAP, an ImageJ/Python macro of broad applicability to one- and two-dimensional systems. Imperfect ; Diffusion ; Reaction–diffusion system ; Computer science ; Statistical physics ; Econometrics ; Thermodynamics ; Economics ; Physics ; Philosophy Mass Spectrometry Techniques and Applications ; Electron Spin Resonance Studies ; Advanced NMR Techniques and Applications
Authors: Clare A. Tovee | Seth B. Wiggin | Natalie T. Johnson | Philip Andrews | Matthew P. Lightfoot
Source: IUCrJ (article, 2025-09-05)
Field: Materials Science — Materials Chemistry

A survey of crystallographic quality metrics from CIFs in the Cambridge Structural Database The Cambridge Structural Database (CSD) is a comprehensive repository of over 1.3 million unique crystallographic datasets, compiled from data deposited at the Cambridge Crystallographic Data Centre (CCDC). Given the vast size of this collection, identifying datasets suitable for reuse in a particular area of study is critical. Traditionally, the R factor has been used as an indicator of structure refinement quality; however, numerous additional metrics can provide valuable insights into the refinement and structural model. Some of these metrics have been extracted from data in the deposited Crystallographic Information Files (CIFs) and added to the CSD. This study surveys these selected crystallographic data items and explores the variability in them between different types of structures. Through a statistical analysis of their values, this work aims to assist with the selection of relevant structures for future reuse. Selection (genetic algorithm) ; Reuse ; Quality (philosophy) ; Data structure ; Data quality X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallization and Solubility Studies
Authors: Teng Ma | Siyu Sun
Source: Bioengineering (article, 2025-12-10)
Field: Materials Science — Biomaterials

The Gastrointestinal Tract: A Unique Battlefield for Bioengineering Delivery Platforms Traditional drug delivery methods for gastrointestinal diseases, including oral and systemic administration, often suffer from degradation, inadequate mucosal absorption, and off-target toxicity. Consequently, these methods result in low bioavailability and suboptimal therapeutic outcomes for localized conditions such as inflammation and early-stage cancer. This review examines the innovative integration of advanced bioengineering platforms with therapeutic gastrointestinal endoscopy to address these delivery challenges. We concentrate on three principal bioengineered platforms: (1) nanoparticle systems (e.g., lipid, polymeric, and inorganic nanoparticles) designed for localized chemotherapy and theranostics; (2) in situ-forming hydrogels that serve as intelligent wound management materials and sustained drug depots; and (3) drug-eluting and biodegradable stents that convert passive luminal scaffolds into active, long-term drug-releasing devices. An analysis of these platforms demonstrates that their synergy with endoscopy facilitates precise, minimally invasive, and sustained local therapy, potentially transforming the treatment landscape for gastrointestinal diseases such as cancer and inflammatory bowel disease. Additionally, we investigate advanced strategies, including active targeting and stimulus-responsive release mechanisms, to enhance spatial precision. Despite promising preclinical advancements, clinical translation encounters challenges related to long-term biocompatibility, scalable manufacturing, regulatory pathways for drug-device combinations, and cost-effectiveness. Ultimately, the convergence of bioengineering and endoscopy presents significant potential to usher in a new era of precise, localized, and sustained micro-invasive treatments in gastroenterology. Drug delivery ; Battlefield ; Therapeutic endoscopy ; Targeted drug delivery ; Endoscopy ; Drug Nanoparticle-Based Drug Delivery ; 3D Printing in Biomedical Research ; Advanced Drug Delivery Systems
Authors: Arash Vaghef‐Koodehi
Source: Journal of Materials Science Materials in Engineering (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Nanoscale engineering of multi-component ZnO varistors: advanced computational modeling and energy absorption optimization Abstract This study presents a comprehensive computational framework for optimizing energy absorption in nano-engineered zinc oxide varistors through systematic multi-component design. A nine-component varistor system comprising ZnO (96.5 mol%), Bi₂O₃ (1.2%), Sb₂O₃ (0.8%), NiO (0.4%), Ce₂O₃ (0.3%), ZrO₂ (0.25%), SnO₂ (0.2%), Co₃O₄ (0.2%), and SiO₂ (0.15%) was computationally modeled using COMSOL Multiphysics to investigate nanoscale energy dissipation mechanisms. The optimized composition demonstrates exceptional nonlinearity coefficient (α = 48) and energy absorption capacity (195 J cm⁻ 3 ), representing 40% enhancement over conventional microstructured designs. Finite element analysis reveals critical electric field concentrations at grain boundaries reaching 6.2 kV cm⁻ 1 with current density variations of 18 × between conducting pathways and matrix regions. Multi-physics simulations incorporating coupled electromagnetic-thermal analysis demonstrate superior thermal stability with peak temperature gradients of 15°C mm⁻ 1 and recovery time constants of 8.2 s. The computational framework provides unprecedented insights into nanoscale conduction mechanisms, enabling precise optimization of varistor compositions for enhanced surge protection applications. Multiphysics ; Varistor ; Nanoscopic scale ; Dissipation ; Absorption (acoustics) ; Thermal stability ; Thermal conduction ; Finite element method ; Nonlinear system ZnO doping and properties ; Lightning and Electromagnetic Phenomena ; Magnetic Properties and Synthesis of Ferrites
Authors: Tushar Parab | Glen Bennet Hermon | Maitreyee Bhave | Shashank Nagarale | Atish Chattopadhyay | Sagar Rajpurkar
Source: Journal of Wildlife Science. (article, 2025-02-02)
Field: Chemistry — Analytical Chemistry

Improved detection and classification of cryptic ‘Phylloscopus’ warblers with integrated computer vision and deep learning method The rapid advancement of image recognition technology, especially with the help of YOLO algorithms, has greatly improved the ability to identify and recognize different species. This research focuses on the Phylloscopus burkii cryptic species complex, distinguished by little phenotypic diversity making precise identification challenging. In order to make it easier to distinguish between members of this complex, we suggest an enhanced YOLOv5-based model that employs a part-based approach that keeps part detection and identification classification apart. Our goals included determining whether images taken in natural settings can produce statistically reliable identifications and examining the variation of image characteristics of the species within the burkii complex. A dataset of 289 annotated images was augmented to build 2,890 photographs for training purposes. After training on Amazon Elastic Cloud, the model achieved a mean average precision (mAP) of 94 and a recall of 97.3. The model demonstrated strong precision and recall metrics across species, and the results showed that it worked best at a confidence threshold of 0.50. P. valentini and P. tephrocephalus, in particular, showed considerable overlap, which sometimes resulted in misidentifications, although P. poliogenys and P. whistleri demonstrated excellent accuracy. We recommend training models based on species geographic distributions to improve identification accuracy. This strategy might successfully lower the rate of erroneous identifications, enhancing the caliber of data produced by citizen science projects. In the end, our results support current efforts to conserve biodiversity by offering a solid framework for automating species identification and guiding ecological study. Our goal is to improve species monitoring and data collection precision by utilizing innovative methods based on machine learning. Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Geography ; Computer vision ; Remote sensing Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Smart Agriculture and AI
Authors: Chongkai Liu | Zhi Xia | Hao Li | Fan Fan | Yong Ma | Huanjun Hu | Can Zhang
Source: Foods (article, 2025-12-02)
Field: Chemistry — Analytical Chemistry

Cross-Temporal Egg Variety and Storage Period Classifications via Multi-Task Deep Learning with Near-Infrared Hyperspectral Imaging Egg variety and storage duration are key determinants of nutritional value, market pricing, and food safety. The similar external appearance of different varieties increases the risk of mislabeling, while inevitable quality deterioration during storage further complicates reliable assessment. These factors underscore the need for non-destructive, cross-temporal detection. However, prolonged storage induces pronounced spectral drift that degrades conventional models, limiting their effectiveness in real-world quality monitoring. To address this issue, we propose the Multi-Task Cross-Temporal Squeeze-and-Excitation Network (MT-CTSE-Net), a deep learning framework that integrates Convolutional Neural Networks (CNN), Squeeze-and-Excitation (SE) channel attention, and Transformer encoders to jointly perform egg variety identification across storage durations and storage period classification. The model extracts local spectral details, enhances channel-wise feature relevance, and captures long-range dependencies, while inter-task feature sharing improves generalization under temporal variation. Evaluated on near-infrared (1000-2500 nm) spectra from three commercial egg varieties (Enshi selenium-enriched, Mulanhu multigrain, Zhengda lutein), MT-CTSE-Net achieved approximately 86% accuracy (F1-score: 86.1%) in cross-temporal variety classification and about 84.2-86.4% in storage-period prediction-surpassing single-task and benchmark multi-task models. These results demonstrate that MT-CTSE-Net effectively mitigates storage-induced spectral drift and provides a robust pathway for non-destructive quality assessment and temporal monitoring in agri-food supply chains. Hyperspectral imaging ; Deep learning ; Convolutional neural network ; Variety (cybernetics) ; Benchmark (surveying) ; Encoder ; Pattern recognition (psychology) ; Limiting Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Yan Tian | Jun Sun | Xin Zhou | Sunli Cong | Chunxia Dai | Lei Shi
Source: Foods (article, 2025-11-09)
Field: Chemistry — Analytical Chemistry

Nondestructive Detection of Soluble Solids Content in Apples Based on Multi-Attention Convolutional Neural Network and Hyperspectral Imaging Technology Soluble solids content is the most important attribute related to the quality and price of apples. The objective of this study was to detect the soluble solids content (SSC) in 'Fuji' apples using hyperspectral imaging combined with a deep learning algorithm. The hyperspectral images of 570 apple samples were obtained and the whole region of apple sample hyperspectral data was collected and preprocessed. In addition, a method involving multi-attention convolutional neural network (MA-CNN) is proposed, which extracts spectral and spatial features from hyperspectral images by embedding channel attention (CA) and spatial attention (SA) modules in a convolutional neural network. The CA and SA modules help the network adaptively focus on important spectral-spatial features while reducing the interference of redundant information. Additionally, the Bayesian optimization algorithm (BOA) is used for model hyperparameter optimization. A comprehensive evaluation is conducted by comparing the proposed model with CA-CNN models, SA-CNN, and the current mainstream models. Furthermore, the best prediction performances for detecting SSC in apple samples were obtained from the MA-CNN model, with an Rp2 value of 0.9602 and an RMSEP value of 0.0612 °Brix. The results of this study indicated that the MA-CNN algorithm combined with hyperspectral imaging technology can be used as an effective method for rapid detection of apple quality parameters. Hyperspectral imaging ; Convolutional neural network ; Pattern recognition (psychology) ; Hyperparameter ; Artificial neural network ; Focus (optics) ; Interference (communication) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Postharvest Quality and Shelf Life Management