jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: L. Thomas | M. Sreekanth
Source: Polymers for Advanced Technologies (article, 2025-08-01)
Field: Materials Science — Polymers and Plastics

Multiwalled Carbon Nanotubes Reinforced Polyvinyl Alcohol Nanocomposites: Synergistic Enhancement on Multifunctional Performance and Deep Learning‐Driven Predictions for Next‐Generation Flexible Electronics ABSTRACT The investigation deals with the enhancement of mechanical, thermal, dielectric, and optical properties of polyvinyl alcohol (PVA) nanocomposites reinforced with varying concentrations of amine‐functionalized multiwalled carbon nanotubes (MWCNTs), specifically ranging from 1 to 7 wt%, respectively. The results demonstrate that incorporation of MWCNTs significantly improves the composites' performance, making them highly suitable for advanced applications in flexible electronics, sensors, and energy storage devices. The mechanical properties demonstrate a critical balance between MWCNTs loading and dispersion, with a peak tensile strength of 50.5 MPa and Shore A hardness of 80.7 with 3 wt% MWCNTs. A robust deep neural network (DNN) model employing the Adam optimizer accurately predicted mechanical behavior, achieving a low mean absolute percentage error (MAPE) and an R 2 value close to unity, underscoring its potential for predictive material design. The electrical percolation threshold was identified at 2 wt% MWCNTs, revealing a remarkable five‐order‐of‐magnitude increase in DC electrical conductivity as compared to pure PVA, establishing a significant benchmark for PVA/MWCNTs systems. Optical and thermal analyses highlighted improvements in absorbance, reduced transmittance, a narrowed bandgap, and superior thermal stability, with the 3 wt% composite emerging as an optimal formulation for high‐performance applications in electronics. Wettability studies revealed a reduction in the hydrophilic nature of PVA in the presence of MWCNTs, where an increase in the contact angle from 48° for pure PVA to 114° at 5 wt% MWCNTs emphasized the influence of MWCNTs on surface interactions. The investigation establishes the critical performance benchmarks for PVA/MWCNTs nanocomposites and demonstrates their potential as multifunctional polymer materials for advanced technological applications. Materials science ; Polyvinyl alcohol ; Nanocomposite ; Carbon nanotube ; Electronics ; Composite material ; Flexible electronics ; Nanotechnology ; Electrical engineering Polymer Nanocomposite Synthesis and Irradiation ; Conducting polymers and applications ; Dielectric materials and actuators
Authors: Huiqiang Hu | Yuping Zhao | Yunpeng Wei | Tingting Wang | Yunlong Mei | Haichuan Ren | Huaxing Xu | Xiaobo Mao | Luqi Huang
Source: Computers and Electronics in Agriculture (article, 2025-03-04)
Field: Chemistry — Analytical Chemistry

Geographical origin discrimination of Ophiopogonis Radix using hyperspectral imaging with multi-scale 3D convolution and transformer Hyperspectral imaging ; Convolution (computer science) ; Artificial intelligence ; Scale (ratio) ; Transformer ; Pattern recognition (psychology) ; Computer science ; Computer vision ; Remote sensing ; Mathematics ; Engineering ; Geography ; Cartography ; Voltage ; Electrical engineering ; Artificial neural network Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Smart Agriculture and AI
Authors: Youzhe Yang | Chunhui Yang | Jie Yang | Ning Wei | Yingyan Zhang
Source: The Journal of Physical Chemistry C (article, 2025-01-23)
Field: Materials Science — Materials Chemistry

Machine Learning for Thermal Conductivity Prediction in Graphene/Hexagonal Boron Nitride van der Waals Heterostructures High-efficient thermal interface materials (TIMs) with tunable thermal conductivity (TC) and excellent insulation are crucial for effective thermal management in advanced electronic components. Graphene/hexagonal boron nitride (h-BN) (GBN) van der Waals (vdW) heterostructure is an ideal candidate for such TIMs since h-BN is insulative and the TC of this heterostructure is highly tunable by strain engineering and defect engineering (e.g., hydrogenation, in-plane strains, 13C isotope, and interlayer coupling strength). Herein, we exploit the joint effects of the factors mentioned above on the TC of GBN vdW heterostructures by using molecular dynamics (MD) simulation and machine learning (ML) models. When combined with other factors, hydrogenation has a dominant influence on the TC of GBN heterostructures. Among the ML models trained by MD results, the genetic programming (GP) model reliably predicts TC with a maximum error of 12.7%. A mathematical formula derived by the GP model connects the TC with the influencing factors to accelerate the targeted structure design. The results suggest that the GP model provides accurate TC predictions for GBN vdW heterostructures and facilitates efficient inverse design of GBN-based TIMs with desired TC. Hexagonal boron nitride ; Graphene ; Materials science ; van der Waals force ; Boron nitride ; Thermal conductivity ; Heterojunction ; Nanotechnology ; Hexagonal crystal system ; Conductivity ; Thermal ; Condensed matter physics ; Composite material ; Optoelectronics ; Crystallography ; Thermodynamics ; Chemistry ; Physical chemistry ; Physics ; Quantum mechanics ; Molecule Thermal properties of materials ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Mohammad Akbar Faqeerzada | Eunsoo Park | Jinsu Lim | Kihyun Kim | Ramaraj Sathasivam | Sang Un Park | Hangi Kim | Byoung–Kwan Cho
Source: Plant Physiology and Biochemistry (article, 2025-10-01)
Field: Chemistry — Analytical Chemistry

Development of multi-sensing technologies for high-throughput morphological, physiological, and biochemical phenotyping of drought-stressed watermelon plants High-throughput plant phenotyping (HTPP) technologies are rapidly transforming plant science by enabling real-time, non-invasive, and large-scale monitoring of complex morphological, physiological, and biochemical traits. However, existing platforms often lack integration across sensing modalities and analytical depth necessary for early and comprehensive phenotypic trait analysis. In this study, we developed a fully automated, multimodal HTPP system combining RGB, shortwave infrared (SWIR) hyperspectral, multispectral fluorescence imaging (MSFI), and thermal imaging to characterize drought-stressed watermelon (Citrullus lanatus) plants. RGB imaging facilitated detailed morphological analysis by extracting color-based traits, quantifying plant height and canopy area, and accurately distinguishing growth stages. SWIR hyperspectral imaging (HSI) enabled non-invasive biochemical assessment by detecting drought-responsive compounds, such as flavonoids, phenolics, and antioxidant activities, while also supporting the classification of stress severity. This spectral profiling revealed key biochemical alterations triggered by water deficit. MSFI liquid crystal tunable filter (LCTF-based) measured chlorophyll a (Chl-a), chlorophyll b (Chl-b), and total chlorophyll (t-Chl) levels, providing critical insights into photosynthetic performance under drought stress. Thermal imaging further enhanced drought assessment by capturing canopy temperature variations, which were used to derive thermal indices for indirect estimation of soil volumetric water content (SVWC). By integrating complementary imaging modalities, the proposed system captured comprehensive phenotypic responses with high predictive accuracy for early detection of drought stress and assessment of plant health. Advanced machine learning (ML) and deep learning (DL) models further enhanced trait extraction and classification, enabling robust analysis of complex, high-dimensional data. This automated, multimodal platform offers scalable, non-invasive crop monitoring, providing precise insights to support drought resilience and precision agriculture. Multispectral image ; Hyperspectral imaging ; Phenomics ; Canopy ; Chlorophyll fluorescence ; Precision agriculture ; Chlorophyll Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advances in Cucurbitaceae Research
Authors: Junxi Yu | Shifeng Qian | Cheng‐Cheng Liu
Source: Physical review. B./Physical review. B (article, 2025-02-27)
Field: Materials Science — Materials Chemistry

General electronic structure calculation method for twisted systems In recent years, two-dimensional twisted systems have gained increasing attention. However, calculating the electronic structures in twisted materials has remained a challenge. To address this, we have developed a general computational methodology that can generate twisted geometries starting from a monolayer structure and obtain a precisely relaxed twisted structure through a machine learning-based method. Then, the electronic structure properties of the twisted material are calculated using the tight-binding (TB) and continuum model methods; thus, the entire process requires minimal computational resources. We introduce the theoretical methods for generating twisted structures and computing their electronic properties, and further provide calculations and brief analyses of the electronic structure properties for several typical two-dimensional materials with different characteristics. This paper serves as a solid foundation for researchers interested in studying twisted systems. Computer science ; Materials science 2D Materials and Applications ; Graphene research and applications ; Topological Materials and Phenomena
Authors: Si‐Ping Gao | Haofan Zhang | Zhiqing Yang | Rongxue Wu | Min Chen | Dandan Zhai | Yang Yu | Yao Qin | Huawei Tao | Peng Li
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2025-08-22)
Field: Chemistry — Analytical Chemistry

A dual-branch CNN-encoder model for rapid geographic traceability of Astragalus using near-infrared spectroscopy Chemistry ; Traceability ; Spectroscopy ; Near-infrared spectroscopy ; Infrared ; Dual (grammatical number) ; Infrared spectroscopy ; Remote sensing ; Organic chemistry ; Optics ; Linguistics ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Benedetta Carrozzini | Liberato De | Cinzia Giannini | Angela Altomare | Rocco Caliandro
Source: Acta Crystallographica Section A Foundations and Advances (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

The phase-seeding method for solving non-centrosymmetric crystal structures: a challenge for artificial intelligence The overall crystallographic process involves acquiring experimental data and using crystallographic software to find the structure solution. Unfortunately, while diffracted intensities can be measured, the corresponding phases - needed to determine atomic positions - remain experimentally inaccessible (phase problem). Direct methods and the Patterson approach have been successful in solving crystal structures but face limitations with large structures or low-resolution data. Current artificial intelligence (AI) based approaches, such as those recently developed by Larsen et al. [Science (2024), 385, 522-528], have been applied with success to solve centrosymmetric structures, where the phase is binary (0 or π). The current work proposes a new phasing method designed for AI integration, applicable also to non-centrosymmetric structures, where the phase is a continuous variable. The approach involves discretizing the initial phase values for non-centrosymmetric structures into a few distinct values (e.g. values corresponding to the four quadrants). This reduces the complex phase problem from a continuous regression task to a multi-class classification problem, where only a few phase seed values need to be determined. This discretization allows the use of a smaller training dataset for deep learning models, reducing computational complexity. Our feasibility study results show that this method can effectively solve small, medium and large structures, with the minimum percentage of phase seeds (three or four points in the interval [0, 2π]), and 10% to 30% of seed symmetry-independent reflections. This phase-seeding method has the potential to extend AI-based approaches to solve crystal structures ab initio, regardless of complexity or symmetry, by combining AI classification algorithms with classical phasing procedures. Discretization ; Phase (matter) ; Artificial intelligence ; Computer science ; Algorithm ; Symmetry (geometry) ; Phaser ; Diffraction ; Mathematics ; Physics ; Geometry ; Optics ; Mathematical analysis Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Enzyme Structure and Function
Authors: Nathan J. Szymanski | Alexander Smith | Pródromos Daoutidis | Christopher J. Bartel
Source: ACS Materials Letters (article, 2025-05-06)
Field: Materials Science — Materials Chemistry

Topological Descriptors for the Electron Density of Inorganic Solids Descriptors play an important role in data-driven materials design. While most descriptors of crystalline materials emphasize structure and composition, they often neglect the electron density─a complex yet fundamental quantity that governs material properties. Here, we introduce Betti curves as topological descriptors that compress electron densities into compact representations. Derived from persistent homology, Betti curves capture bonding characteristics by encoding components, cycles, and voids across varied electron density thresholds. Machine learning models trained on Betti curves outperform those trained on raw electron densities by an average of 33 percentage points in classifying structure prototypes, predicting thermodynamic stability, and distinguishing metals from nonmetals. Shannon entropy calculations reveal that Betti curves retain comparable information content to electron density while requiring 2 orders of magnitude less data. By combining expressive power with compact representation, Betti curves highlight the potential of topological data analysis to advance materials design. Topology (electrical circuits) ; Electron ; Physics ; Electron density ; Mathematics ; Quantum mechanics ; Combinatorics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Xiaoyu Zhao | Yuxia Wang | Yuting Liu | Xinyi Chen | Mingyu Cheng | Yaxin Wang | Jiahong Wen | Renxian Gao | Kun Zhang | Fengyi Zhang | Rufei Cui | Yongjun Zhang | Zengyao Wang | Bin Ai
Source: Advanced Science (article, 2025-04-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Gradient Nanostructures and Machine Learning Synergy for Robust Quantitative Surface‐Enhanced Raman Scattering Surface-Enhanced Raman Scattering (SERS) holds significant promise for trace-level molecular detection but faces challenges in achieving reliable quantitative analysis due to signal variability caused by non-uniform "hot spots" and external factors. To address these limitations, a novel SERS platform based on gradient nanostructures is developed using shadow sphere lithography, enabling the acquisition of diverse spectral features from a single analyte concentration under identical conditions. The gradient design minimizes fabrication variability and enhances spectral diversity, while the machine learning (ML) model trained on the multi-spectral dataset significantly outperformed traditional single-spectrum approaches, with the test Mean Squared Error (MSE) reduced by 84.8% and the coefficient of determination (R<sup>2</sup>) improved by 61.2%. This strategy captures subtle spectral variations, improving the precision, robustness, and reproducibility of SERS-based quantification, paving the way for its reliable application in real-world scenarios. Robustness (evolution) ; Raman scattering ; Reproducibility ; Computer science ; Materials science ; Mean squared error ; Biological system ; Scattering ; Raman spectroscopy ; Artificial intelligence ; Nanotechnology ; Optics ; Physics ; Chemistry ; Mathematics ; Statistics Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Marlon Rodrigues | Everson Cézar | Gláucio Leboso Alemparte Abrantes dos Santos | Amanda Silveira Reis | Roney Berti de Oliveira | Leticia de Melo Teixeira | Marcos Rafael Nanni
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2025-02-19)
Field: Chemistry — Analytical Chemistry

Unveiling the potential of Brachiaria ruziziensis: Comparative analysis of multivariate and machine learning models for biomass and NPK prediction using Vis-NIR-SWIR spectroscopy Chemistry ; Brachiaria ; Biomass (ecology) ; Multivariate statistics ; Spectroscopy ; Multivariate analysis ; Botany ; Agronomy ; Machine learning ; Forage Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Nguyễn Thành Tiên | Phạm Thị Bích Thảo | Duy Khanh Nguyen | Le Nhat Thanh | Vo Khuong Dien
Source: Journal of Applied Physics (article, 2025-02-27)
Field: Materials Science — Materials Chemistry

Thermoelectric properties of penta-InP5: A first-principles and machine learning study Smart wearable devices that harvest energy from ambient sources, such as body heat, are gaining significant attention due to their potential in diverse applications. Thermoelectric (TE) materials, which convert thermal energy to electrical power, are critical for these devices, yet achieving both high TE performance and mechanical flexibility remains a significant challenge. Here, we investigate the TE properties of the penta-InP5 monolayer, a novel two-dimensional material, using first-principles calculations integrated with machine learning potentials. We show that penta-InP5 achieves a remarkable figure of merit, with values of 0.51 and 0.42 for hole and electron doping, respectively, at room temperature. Additionally, the material demonstrates remarkable mechanical properties, with an in-plane stiffness of 52 N/m and a fracture strain of 23% for the uniaxial strain. These findings suggest that penta-InP5 is a promising candidate for flexible, high-performance TE applications, advancing the potential of wearable energy-harvesting devices. Thermoelectric effect ; Materials science ; Thermoelectric materials ; Engineering physics ; Condensed matter physics ; Computer science ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Advanced Semiconductor Detectors and Materials
Authors: Nutcha Taneepanichskul | Helen C Hailes | Mark Miodownik
Source: UCL Open Environment (article, 2025-02-24)
Field: Chemistry — Analytical Chemistry

Using hyperspectral imaging and machine learning to identify food contaminated compostable and recyclable plastics With the increasing public legislation aimed at reducing plastic pollution, compostable plastics have emerged as an alternative to conventional plastics for some food packaging and food service items. However, the true value of compostable plastics can only be realised if they do not enter the environment as contaminants but instead are processed along with food waste using industrial composting facilities. Distinguishing compostable plastics from other plastics in this waste stream is an outstanding problem. Currently, near-infrared technology is widely used to identify polymers, but it falls short in distinguishing plastics contaminated with food waste. This study investigates the application of hyperspectral imaging to address this challenge, enhancing the detection and sorting of contaminated compostable plastics. By combining hyperspectral imaging with various machine learning algorithms we show it is possible to accurately identify and classify plastic packaging with food waste contamination, achieving up to 99% accuracy. The study also measures the impact of plastic features such as darkness, size and level of contamination on model performance, with darkness having the most significant impact. The developed machine learning model can detect plastic with higher levels of contamination more accurately compared to our previous study. Implementing hyperspectral imaging in waste management systems can significantly increase composting and recycling rates, and improve the quality of recycled products. This advanced approach supports the circular economy by ensuring that both compostable and recyclable plastics are effectively processed and recycled, minimising environmental impact. Physics Spectroscopy and Chemometric Analyses ; Vehicle License Plate Recognition ; Currency Recognition and Detection
Authors: Elena Bandini | Ardiana Kajtazi | Roman Szücs | Frédéric Lynen
Source: TrAC Trends in Analytical Chemistry (review, 2025-02-25)
Field: Chemistry — Spectroscopy

The role and choice of molecular descriptors for predicting retention times in HPLC: A comprehensive review High-performance liquid chromatography ; Retention time ; Chromatography ; Psychology ; Computer science ; Computational biology ; Chemistry ; Biology Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: A. H. M. Iftekharul Ferdous | Md. Safiul Islam | Abdullah Al Mamun | Mobasser Hamid Reza | Md. Jakir Hossen | Md. Shamim Anower
Source: Journal of Hazardous Materials Advances (article, 2025-09-11)
Field: Chemistry — Spectroscopy

Terahertz PCF sensor for explosive detection: A machine learning approach to nitroglycerine and royal demolition analysis • High Sensitivity Detection: The proposed square hollow-core PCF sensor achieves exceptional relative sensitivity—up to 98.09% for Nitroglycerin and 88.25% for RDX—within the 1 to 2.8 THz range. • Low Loss and Broad Effective Area: Optimized structural parameters ensure minimal effective material loss and a large effective area, enhancing the sensor’s overall optical performance. • FEM-Based Numerical Analysis: The sensor’s performance is validated using Finite Element Method (FEM) simulations, confirming its precision in explosive detection. • Machine Learning Integration: A Random Forest Regressor is employed to predict sensor behavior, achieving a perfect R² score and enabling accurate forecasting of parameters like sensitivity and EML. • Fabrication Readiness: The sensor design is compatible with current fabrication technologies, making it practical for real-world deployment in security and safety applications. This article presents a square hollow core Photonic Crystal Fiber (PCF) sensor developed for high relative sensitivity detection of explosives (Nitroglycerin and Royal Demolition Explosive (RDX)) in the terahertz region (1 THz to 2.8 THz). The numerical sensing capabilities are assessed utilizing the finite element technique(FEM). We have attained enhanced relative sensitivity with negligible loss for detecting Nitroglycerine and RDX through the optimization of structural factors. The maximum relative sensitivity achieved is 98.09 % for Nitroglycerine and 88.25 % for RDX at 2 THz. Additionally, we have achieved little effective material loss (EML) and an extensive effective area. The proposed sensor design is compatible with current fabrication technologies, ensuring practical feasibility. Furthermore, the prediction was conducted with the Random Forest Regressor. We have attained optimal accuracy of prediction with a unity R 2 score, and this model may be utilized for predicting much behaviour, including relative sensitivity and EML for frequency. Explosive material ; Sensitivity (control systems) ; Finite element method ; Fabrication ; Terahertz radiation ; Demolition Spectroscopy and Laser Applications ; Terahertz technology and applications ; Photonic Crystal and Fiber Optics