jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Murugesan Palaniappan | Mohan Kumar Anand Raj | P. Manoj Kumar | Dawit Tafesse Gebreyohannes
Source: Scientific Reports (article, 2026-03-17)
Field: Materials Science — Polymers and Plastics

Investigation on the mechanical behavior of coconut leaf sheath and midrib of coconut leaf reinforced epoxy composites Over the last two decades, researchers have focused on natural reinforcement composites as choice to conventional and man-made fiber composites due to their enhanced properties. While many research has studied the mechanical characteristics of renewable fiber composites, only limited work has been done on coconut fiber–based composites. In particular, no research has explored the reinforcement of two specific coconut fibers: coconut leaf sheath (CLS) and the midrib of coconut leaf (MCL). The aim of this study is to evaluate the mechanical characteristics of composites incorporated with CLS and MCL. Composite specimens were prepared by reinforcing MCL at 10%, 15% and 25% in CLS (20%) by hand moulding process. The specimens were tested in terms of tensile strength, flexural strength, impact resistance, and interlaminar shear strength (ILSS). The results show that the specimen with 25% MCL has the highest mechanical properties among all the combinations. ILSS testing confirms better fiber and matrix bonding at 25% MCL and hence better load transfer and strength. Similarly, flexural and impact tests show that the load-bearing capacity and energy absorption are improved through the use of higher MCL content to resist bending and sudden loads. However, water absorption has also been found to increase with an increase in fiber content because of the hydrophilic nature of MCL, which may influence moisture resistance. SEM analysis has been analyzed to identify the interfacial bonding and structural integrity of the material. The diffusion coefficient of the water uptake was determined using Fick’s thin plate approximation method. FTIR analysis shows the existence of important functional groups, which indicates that the fiber-matrix interaction and epoxy curing are successful. Although increased fiber content can lead to some processing difficulties, the 25% MCL composite provides the best balance between strength, flexibility and durability and is a promising material for structural and load-bearing applications. Flexural strength ; Absorption of water ; Epoxy ; Ultimate tensile strength ; Fiber ; Composite number ; Natural fiber ; CLs upper limits ; Moisture Natural Fiber Reinforced Composites ; Bamboo properties and applications ; Geotechnical Engineering and Soil Stabilization
Authors: Muhammad Amir | Mehfooz Ul Haq | Faria Shahid | Fatima Anjum | Samina Tanvir | Yaqoob Sultan | Warda Javed
Source: Journal of Plant Stress Physiology (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Physiological and biochemical responses of chickpea (Cicer arietinum) to endophytic fungi and silver nanoparticles in relation to Helicoverpa armigera Chickpea (Cicer arietinum L.), an important pulse crop of the Fabaceae family, is widely cultivated due to its high protein content and nutritional value. In Pakistan, major chickpea-growing regions include the Thal desert, Layyah, Bhakkar, Chowk Azam, and Chobara. However, chickpea production is severely constrained by insect pests, particularly Helicoverpa armigera (cotton bollworm), a destructive chewing pest that damages stems, delays crop growth, and reduces yield potential. Sustainable and eco-friendly approaches to pest control are therefore essential. In the present study, green-synthesized silver nanoparticles (AgNPs) in combination with Metarhizium anisopliae were investigated for their physiological, biochemical, and pesticidal effects on chickpea under controlled conditions. Chickpea plants were treated with two concentrations of AgNO3 (5% and 10%) in combination with M. anisopliae (2% and 4%) through foliar application prior to pest infestation. The ingestion of AgNPs by H. armigera larvae caused gut damage and subsequent mortality, thereby protecting the plants. Mortality rates increased with both concentration and exposure time, reaching 20%, 40%, 60%, and 80% after 24, 48, 72, and 96 hours, respectively. In addition to pest suppression, treated plants exhibited significant improvements in physiological and biochemical attributes. Biomass accumulation, root length, and shoot height were enhanced, while photosynthetic pigments (chlorophyll a, chlorophyll b, and carotenoids) showed a marked increase compared to controls. Furthermore, biochemical assays revealed elevated levels of reactive oxygen species (MDA and H₂O₂) alongside enhanced activities of enzymatic antioxidants (SOD, POD, CAT) and non-enzymatic antioxidants (TSP), indicating activation of the plant defense system. The study, conducted in a completely randomized design with three replicates, demonstrates that the synergistic application of silver nanoparticles and M. anisopliae offers a promising, safe, and environmentally sustainable strategy for managing H. armigera infestation in chickpea while simultaneously promoting plant growth and physiological performance. Helicoverpa armigera ; PEST analysis ; Metarhizium anisopliae ; Plant use of endophytic fungi in defense ; Crop ; Silver nanoparticle ; Metarhizium ; Shoot ; Beauveria Nanoparticles: synthesis and applications ; Entomopathogenic Microorganisms in Pest Control ; Insect Pest Control Strategies
Authors: Sanket Jantre | Nathan M. Urban | Weiguo Yin | Niraj Aryal
Source: arXiv (Cornell University) (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Data-efficient surrogate modeling of spectral functions using Gaussian processes: An application to the $t$-$t'$-$t''$-$J$ model Spectral functions encode key many-body information but are costly to compute with high fidelity. Machine-learning surrogates have emerged as a powerful alternative, yet many approaches require large training datasets. We develop a data-efficient surrogate for spectral functions using the $t$-$t'$-$t''$-$J$ model, which describes the motion of a hole in a quantum antiferromagnet. Using $\sim$ 10$^5$ self-consistent Born approximation-based spectra from Lee, Carbone and Yin (Phys. Rev. B 107, 205132 (2023)), we train a deep-kernel Gaussian process surrogate model with sparse variational inference (DKL-SVGP) using only 10% of the available training spectra. We benchmark against feed-forward neural networks (FFNN) trained on the same reduced subset and on the full dataset. The proposed DKL-SVGP model consistently outperforms the reduced-data FFNN and, despite using only 10% of the training spectra, achieves spectrum-wise errors within the same order-of-magnitude as the full-data FFNN baseline. Worst-tail diagnostics show improved fidelity on difficult spectra, while peak-level analysis indicates that DKL-SVGP recovers dominant peak heights with comparable accuracy and improves peak-location agreement under a matched-peak evaluation that mitigates rare peak-swapping cases. Overall, these results highlight GP-based surrogates as a competitive and data-efficient approach for spectral-function prediction in scarce-data regimes. Gaussian process ; Benchmark (surveying) ; Inference ; Gaussian ; Artificial neural network ; Process (computing) ; Fidelity Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Tianqing Zhang | Ce Wang | V.E. Kuzmichev | Xiaolong Dond | Lin Xing
Source: Preprints.org (preprint, 2026-01-13)
Field: Materials Science — Polymers and Plastics

Digital Reconstruction of Hong'an Homespun Using AI and Semantic Differential Method This study develops an innovative method for the attribution and visual reconstruction of hand-woven fabrics using artificial intelligence, employing Chinese Hong&amp;#039;an Homespun as a case study. The paper proposes a comprehensive algorithm integrating microscopic analysis, physical micro-model creation, and bimodal prompt engineering. The semantic differential method with a five-point scale was applied for objective evaluation of visual replica of historical fabrics. Comparative testing of AI models (Midjourney, ChatGPT, Qwen3, DouBaoAI, HailuoAI) revealed significant differences in their ability to reproduce characteristic features of hand weaving. The results demonstrate the superiority of detailed prompts with precise quantitative parameters and confirm the effectiveness of micro-models as visual anchors. The research establishes new standards in the digital documentation of cultural heritage and opens prospects for preserving traditional textile techniques. The most successful AI are Midjourney and ChatGPT have achieved an average score of 0.88 on the semantic scale, confirming the practical applicability of the method. Semantic differential ; Scale (ratio) ; Replica ; Documentation ; Visualization ; Differential (mechanical device) ; Visual inspection Textile materials and evaluations ; Generative Adversarial Networks and Image Synthesis ; Fashion and Cultural Textiles
Authors: Deuk-Hwan Lee | Gi-Jun Lee | Hwan-Hui Lim | Seok Yoon
Source: Scientific Reports (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Evaluation of bentonite–graphite (B–G) mixture homogeneity using visible-near infrared spectroscopy and its impact on B–G buffer block manufacturability The homogeneity of bentonite-graphite (B-G) mixtures is essential for the performance of engineered barrier systems in high-level radioactive waste (HLW) repositories. Uniform graphite distribution enhances thermal conductivity while maintaining the protective functions of bentonite. This study investigates the feasibility of using visible and near-infrared (VIS-NIR) spectroscopy combined with partial least squares regression (PLSR) to assess B-G mixture homogeneity. Small-quantity mixtures containing 0-5 wt% graphite were prepared, and hyperspectral data (400-1,000 nm) were collected. A PLSR model was developed to predict graphite content from the spectral data, achieving high accuracy (R² = 0.992, RMSE = 0.151 wt%) using five latent variables. The model was applied to large-scale mixtures blended using a double helix screw mixer. Samples collected at different time intervals were evaluated to determine the required mixing time for achieving homogeneity. After 120 min of mixing, statistical tests confirmed uniform graphite distribution. Buffer blocks were then fabricated using both homogeneous and inhomogeneous mixtures. Blocks from homogeneous mixtures showed no visible defects, whereas those from inhomogeneous mixtures exhibited edge spalling and lateral cracking. These results demonstrate that VIS-NIR spectroscopy with PLSR is a reliable, nondestructive method for evaluating mixture homogeneity and offers practical advantages for quality control in HLW buffer material production. Homogeneity (statistics) ; Partial least squares regression ; Graphite ; Analytical Chemistry (journal) ; Spectroscopy ; Design for manufacturability ; Buffer (optical fiber) ; Chemometrics Graphite, nuclear technology, radiation studies ; Soil and Unsaturated Flow ; Engineering and Material Science Research
Authors: Muzamil Ayoub | Ana Sakura Zainal Abidin | K. Lias | Imtiyaz Akbar Najar | RASLI MUSLIMEN | Mohd Azrin Mohd Said | Vannessa Lawai
Source: Sugar Tech (article, 2026-04-01)
Field: Chemistry — Inorganic Chemistry

Artificial Neural Network-Based Prediction of Nipa Sugar Production in Sarawak, Malaysia Abstract Nipa palm ( Nypa fruticans Wurmb) plays a vital role in the socioeconomic development of coastal communities in Sarawak, Malaysia, where its sap is traditionally used for sugar production. However, nipa sugar production is highly variable due to fluctuating environmental conditions, making reliable forecasting challenging. Current prediction methods rely on empirical observations and historical trends, which fail to capture the complex, nonlinear interactions between climatic factors and sugar yield. This study addresses the need for a more accurate forecasting model by developing a feedforward artificial neural network (FFANN) to predict nipa sugar production using daily data collected for twelve months from Kampung Tambirat, Sarawak. The FFANN model incorporates key environmental variables, including temperature, humidity, wind speed, atmospheric pressure and sap yield, and is trained using the resilient backpropagation (RPROP +) algorithm. The model’s performance was compared to classical time series models (ARIMA and seasonal naïve) and a decision tree regression model. Key results show that the FFANN model outperformed the benchmark models with a coefficient of determination (R 2 ) of 0.73 and a normalized root mean square error (RMSE) of 0.097. Sensitivity analysis identified temperature, wind speed and humidity as the most influential factors on sugar production. The proposed FFANN model provides a robust decision support tool for nipa sugar producers, offering more accurate predictions for harvest planning and resource allocation. Future research could expand the robustness of the model by integrating multi-year datasets and IoT-based monitoring systems to enhance real-time forecasting capabilities. Artificial neural network ; Sugar ; Decision tree ; Backpropagation ; Production (economics) ; Benchmark (surveying) ; Robustness (evolution) ; Mean squared error ; Feedforward neural network Coconut Research and Applications ; Oil Palm Production and Sustainability ; Date Palm Research Studies
Authors: Debarati Roy | Anirban Ghosal | Vusala Nabi Jafarova | JEYRAN R. SULTANOVA | SEVDA RZAYEVA
Source: Romanian Journal of Physics (article, 2026-03-15)
Field: Materials Science — Materials Chemistry

Machine Learning and DFT-based Combined Framework for Predicting Transmission Spectra of Quantum-Confined SiC Nanotube Machine Learning enables accurate and efficient prediction of electronic properties in nanostructures, offering a faster alternative to time-intensive abinitio methods. This study focuses on the transmission spectra of quantum-confined SiC nanotubes, which carry key electronic information. Due to their exceptional hardness, thermal stability, and wear resistance, SiC nanotubes are ideal candidates for investigation. It is found that quantum transmission is highly sensitive to confinement effects. A learnable real-space Hamiltonian model is proposed to accurately predict transmission spectra, incorporating minimal changes due to confinement and supporting arbitrary k-point sampling. A large dataset is used to train ML models, with Density Functional Theory providing reference calculations. Among various regression learners tested, the Fine Tree algorithm yields the most accurate and reliable predictions. The study demonstrates a robust methodology combining DFT and ML to efficiently predict transmission properties of nano-dimensional SiC structures. Hamiltonian (control theory) ; Transmission (telecommunications) ; Spectral line ; Density functional theory ; Thermal ; Ideal (ethics) ; Key (lock) ; Nanotube Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Carbon Nanotubes in Composites
Authors: Lei Kang | Huaping Luo | Xueting Ma | Jinlong Yu | Hongyang Liu | YueSen Tong
Source: Scientific Reports (article, 2026-02-20)
Field: Chemistry — Analytical Chemistry

Study on the effect of moisture content on the spectral detection of soluble solids in apricot To reduce the influence of moisture content variation on the spectral detection of soluble solid content (SSC) and achieve rapid, non-destructive detection of SSC, as well as optimize the processing parameters of apricot products, improve detection efficiency and quality control, this study, based on spectral detection technology, combined with the multiple scatter correction (MSC) preprocessing method, concentration residual method for outlier removal, competitive adaptive reweighted sampling method for feature band selection and partial least squares (PLS) method, constructed SSC detection models for apricots at different moisture contents, and explored the impact of moisture content on the detection model effect of SSC in apricot. This study showed that as moisture content decreased, both the values of the valley depth and valley area in the first-derivative spectrum near 970 nm, as well as the peak height and peak area of the reflection peak, gradually diminished, reflecting the progressive weakening of moisture-related spectral characteristics. Furthermore, within the 450–1450 nm wavelength range, SSC prediction accuracy significantly improved with decreasing moisture content. Among the four moisture intervals, the optimal moisture range for apricot SSC detection is 30%-48%. The model exhibits an Rp² of 0.9690, an RMSEP of 0.5712°Brix, and an RPD of 5.7989, meeting the requirements for high-precision quantitative analysis. This study reveals variations in the hyperspectral SSC prediction performance across different moisture content ranges during the apricot drying process, providing a theoretical basis and technical reference for subsequent offline quality assessment and process monitoring of dried apricots. Hyperspectral imaging ; Water content ; Partial least squares regression ; Moisture ; Residual ; Sampling (signal processing) ; Detection limit ; Spectral signature Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Color Science and Applications
Authors: Manav Barot | J. H. Kim | Daehan Won | Sang Won Yoon
Source: The International Journal of Advanced Manufacturing Technology (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

PhyViT-GAN: Physics-Guided MobileViT-GAN for precise self-alignment image generation Abstract Surface Mount Technology (SMT) has transformed modern electronics manufacturing, enabling the production of compact, reliable, and high-performance devices. Among the critical phenomena in SMT, the self-alignment effect during reflow soldering plays a pivotal role in ensuring precise component placement through the principles of energy minimization. Despite its importance, current research primarily focuses on numerical predictions of self-alignment metrics, leaving a significant gap in generating visual representations, such as Post-Automated Optical Inspection (AOI) images, to comprehensively depict alignment outcomes. In this study, we introduce a novel framework that combines physics-based modeling with advanced deep learning to address this gap. Leveraging data from Solder Paste Inspection (SPI), Pre-Automated Optical Inspection (Pre-AOI), and Post-Automated Optical Inspection (Post-AOI) stages, we developed an energy minimization-based physics model to produce displacement vectors capturing self-alignment dynamics. We integrated these inputs, along with key component attributes, into PhyViT-GAN, a Physics-Guided Variational Autoencoder, and MobileViT as the discriminator in adversarial training for generating high-fidelity Post-AOI images. Our approach effectively visualizes the self-alignment effect, achieving a Structural Similarity Index Measure (SSIM) of 0.8348 and a Learned Perceptual Image Patch Similarity (LPIPS) score of 0.0917, demonstrating high structural accuracy and perceptual fidelity. This work represents the first application of deep learning to generate Post-AOI images for visualizing self-alignment in SMT. This framework bridges the gap between physics-driven predictions and visual inspections, offering transformative potential for SMT manufacturing. It enables enhanced defect detection, virtual prototyping, and process optimization, laying the foundation for future advancements in automated and data-driven electronics assembly. Component (thermodynamics) ; Deep learning ; Process (computing) ; Discriminator ; Transformative learning ; Key (lock) ; Electronics ; Margin (machine learning) ; Energy (signal processing) ; Semantic gap Machine Learning in Materials Science ; Physical Unclonable Functions (PUFs) and Hardware Security ; Industrial Vision Systems and Defect Detection
Authors: Zhanou Liu | Yuhao Chen | Yingjin Ma | Xiao He | Yuxin Deng
Source: ArXiv.org (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Multiconfiguration Pair-Density Functional Theory Calculations of Ground and Excited States of Complex Chemical Systems with Quantum Computers Accurately describing strong electron correlation in complex systems remains a prominent challenge in computational chemistry as near-term quantum algorithms treating total correlation often require prohibitively deep circuits. Here we present a hybrid strategy combining the Variational Quantum Eigensolver with Multiconfiguration Pair-Density Functional Theory to efficiently decouple correlation effects. This approach confines static correlation to a compact multireference quantum state while recovering dynamic correlation through a classical on-top density functional using reduced-density information. By enabling self-consistent orbital optimization, the method significantly reduces quantum resource overheads without sacrificing physical rigor. We demonstrate chemical accuracy on standard benchmarks by reproducing C$_2$ equilibrium bond lengths and benzene excitation energies with mean absolute errors of 0.006 Å and 0.048 eV respectively. Most notably, for the strongly correlated Cr$_2$ dimer requiring a large complete active space (48e, 42o), the framework yields a bound potential-energy curve and recovers qualitative dissociation behavior despite realistic hardware noise. These results establish that separating correlation types provides a practical route to reliable predictions on near-term quantum hardware. Density functional theory ; Excited state ; Quantum chemistry ; Quantum ; Electronic correlation ; Coupled cluster ; Dissociation (chemistry) ; Hybrid functional ; Quantum computer ; Orbital-free density functional theory Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum Computing Algorithms and Architecture
Authors: Majid Hussain | Musib Siddique | Farhan Hameed Malik | Zunaib Maqsood Haider | Ghulam Amjad Hussain
Source: Eng—Advances in Engineering (article, 2026-01-10)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Prediction of Breakdown Voltage in High-Voltage Transmission Lines Under Ambient Conditions Reliability and safety of high-voltage transmission lines are essential for stable and continuous operation of a power system. Environmental factors such as pressure, temperature, surface contamination, humidity, etc., significantly affect the dielectric strength of air, often causing unpredictable voltage breakdowns. This research presents a novel machine learning-based predictive framework that integrates Paschen’s Law with simulated and empirical data to estimate the breakdown voltage (Vbk) of transmission lines in various environmental conditions. The main contribution is to demonstrate that data-driven prediction of breakdown voltage (Vbk) using a hybrid machine learning model is in agreement with physical discharge theory. The model achieved root mean square error (RMSE) of 5.2% and mean absolute error (MAE) of 3.5% when validated against field data. Despite the randomness of avalanche breakdown, model predictions strongly match experimental measurements. This approach enables early detection of insulation stress, real-time monitoring, and optimises maintenance scheduling to reduce outages, costs, and safety risks. Its robustness is confirmed experimentally. Overall, this work advances the prediction of avalanche breakdown behaviour using machine learning. Randomness ; Electric power transmission ; Robustness (evolution) ; Breakdown voltage ; Voltage ; Partial discharge ; Root mean square ; Predictive modelling ; Dielectric strength High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Lightning and Electromagnetic Phenomena