jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cady A. Lancaster | Pamela J. McClure | Erin Price | Victor Deklerck
Source: Wood Science and Technology (article, 2025-06-05)
Field: Chemistry — Organic Chemistry

Impact of heat treatment on database recall and metabolomic fingerprints used for timber identification employing direct analysis in real time time-of-flight mass spectrometry Abstract Illegal logging is a profitable transnational crime, prompting the development of global reference databases to support tools for identifying wood species and harvest location. While these tools are effective for raw timber, they have not been systematically evaluated for manufactured wood products which undergo high temperature, pressure, and steam treatments. Although it is known that these treatments alter wood's chemical composition, no chemical technique has demonstrated its ability to generalize measurements from raw wood to its manufactured form. This paper examines the impact of heat treatments and plywood manufacturing on metabolomic fingerprints and database recall for timber species identification. Processes included plywood manufacturing of Betula pendula and lab-based heat treatments (72 °C to 250 °C) for Cedrela odorata and Dalbergia nigra (both CITES-listed), as well as Diospyros crassiflora and Araucaria cunninghamii . The database recall for D. crassiflora and D. nigra , both dark woods, were largely unaffected by temperature whereas the database recall decreased with increasing temperatures for C. odorata (species-level database recall dropping below 30% after 24 h at 200 °C and continuing to decline with longer treatments) and A. cunninghamii (species-level database recall dropping below 10% after 24 h at 150 °C and continuing to decline with longer treatments) and due to manufacturing processes of the B. pendula plywood (manufacturer, using a single tree for each plywood batch. The logs were soaked and pressed at 120–135 °C). The findings underscore the need for (1) complementary identification methods for manufactured wood products and (2) further research into how manufacturing processes impact the effectiveness of identification and traceability methods under development. Database ; Betula pendula ; Raw material ; Pulp and paper industry ; Engineering ; Environmental science ; Biology ; Botany ; Computer science ; Ecology Wood and Agarwood Research ; Cultural Heritage Materials Analysis ; Identification and Quantification in Food
Authors: Ahmad Fazlan Ahmad Zamri | Azmir Mamat Nawi | Nur ‘Aisyah ‘Awatif Binti Mohamad ‘Ashri
Source: International Journal of Research and Innovation in Social Science (article, 2025-11-04)
Field: Materials Science — Polymers and Plastics

Biodegradable Materials and Renewable Materials Innovation with Eggshell Powder in Sustainable Product Design This research addresses the growing global challenge of plastic waste, resource depletion, and industrial reliance on non-renewable materials by exploring the potential of eggshell powder (ESP) as a biodegradable and renewable alternative in sustainable product design. Although significant progress has been made in biopolymer and composite development, much of the research remains fragmented, with limited integration of material science findings into industrial design frameworks. The aim of this study is to conceptualize the role of ESP in advancing biodegradable materials and renewable materials innovation within the context of sustainable product design. Adopting a mixed-method conceptual approach, the study synthesizes recent empirical findings, analyzes laboratory-based performance data, and reviews relevant theoretical frameworks including eco-design and circular economy models. Findings indicate that ESP can enhance barrier and thermal properties in bioplastics, improve compressive strength in cementitious systems at low substitution levels, and serve as a precursor for higher-value applications such as hydroxyapatite production. However, performance trade-offs remain, particularly in tensile strength and workability, and challenges persist in scaling, dispersion, and user-centered adoption. The study implies that ESP has strong potential for integration into sustainable manufacturing and design practices, particularly in Malaysia where eggshell waste is abundant. It concludes that bridging technical optimization with circular economy strategies and design-for-sustainability principles will be critical to transforming ESP from laboratory experimentation into viable industrial practice. Circular economy ; Sustainable design ; Cementitious ; Renewable resource ; Context (archaeology) ; Renewable energy ; Sustainable development ; Product design ; Resource efficiency Natural Fiber Reinforced Composites ; biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging
Authors: Abhijith Jayan Abhijith Jayan | Sreerag S Sreerag S | Thanuja T Thanuja T | Vipin Joseph Vipin Joseph | Girish Kumar K Girish Kumar K
Source: International Journal of Pharmaceutical Research and Applications (article, 2025-04-01)
Field: Chemistry — Physical and Theoretical Chemistry

Advanced Instrumental Method of Analysis in Analytical Toxicology Laboratory for Toxicological Analysis Major advances in analytical toxicology followed the introduction of spectroscopic and chromatographic techniques. Thin layer chromatography remains important together with some spectrophotometric and other tests. However, gas and high-performance liquid chromatography together with variety of enzyme immunoassay techniques are now widely used. Advanced Instrumentation techniques that include UPLC and its hyphenated systems with MS ,UHPLC and its hyphenated systems with MS, GC and its hyphenated systems with MS, headspace and FID. Analytical Toxicology being emerging as an emergency critical care service, these analytical techniques are to be used for better patient care in diagnosis and treatment of various poisoned victims. Toxicology ; Biochemical engineering ; Engineering ; Biology Various Chemistry Research Topics ; Analytical Chemistry and Chromatography
Authors: Fátima Pardo‐Ávila | Liv Weiner | Paulina Cabral | Nicholas C. Corsepius | Frédéric Poitevin | Michael Levitt
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-02-19)
Field: Materials Science — Materials Chemistry

PDBCleanV2: A Python Library for Generating Consistent Structure Datasets Abstract The number of structures in the Protein Data Bank has grown rapidly in recent years. Nonetheless, comparing structures of a specific system often proves challenging due to variant nomenclature, origins from different species, presence of various ligands, and missing atoms or chains. To address these issues, we have developed PDBCleanV2, a Python package that enables users to create consistent datasets of structures, simplifying comparison among structures. This library generates individual files for each molecule in a structure file, corrects labeling errors, and standardizes chain names and numbering. Our aim is to provide researchers with consistent datasets that streamline their analysis. The source code, installation instructions, and tutorials can be found in PDBCleanV2’s GitHub repository and Zenodo accessible at https://github.com/fatipardo/PDBClean-0.0.2/ and https://doi.org/10.5281/zenodo.14014241 , respectively. Python (programming language) ; Computer science ; Programming language ; Artificial intelligence ; Information retrieval Machine Learning in Materials Science
Authors: Leung Tsang | Zhenming Huang | Jongwoo Jeong
Source: npj Metamaterials (article, 2025-11-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fast hybrid multiple scattering theory for full-wave simulation of metasurface with substrate It is important to characterize accurately the metasurface device for the desired performance using solutions of Maxwell equations for 3D problems. In this paper, we perform a full-wave simulation of metasurfaces with thousands of scatterers above a dielectric substrate, which has not been done previously. To account for the substrate, we developed a new version of the Fast Hybrid Multiple Scattering Theory Method (FHMSTM) by combining Vector Plane Waves (VPW) and Vector Spherical waves (VSW). Transformations between VPW and VSW are used in Foldy–Lax multiple scattering equations. The results are illustrated for two examples: (i) Orbital Angular Momentum (OAM) metasurfaces and (ii) metamirror metasurfaces. We illustrate the case of 5024 silicon elliptical nanopillar scatterers above a dielectric substrate. The CPU and memory requirements are, respectively 1023.2 s and 100 Mb on a standard personal computer. This simulation was previously very difficult for commercial software. Scattering ; Plane wave ; Nanopillar ; Dielectric ; Plane (geometry) ; Substrate (aquarium) ; Angular momentum Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Electromagnetic Scattering and Analysis
Authors: Daniel-Gheorghe Vela | Ion Vela | Călin-Octavian Miclosină
Source: Robotica & Management (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Considerations on the Constructive Design of a Robotic Prehension Device Driven by Shape Memory Alloy Elements This paper presents the 3D model of a robotic prehension device driven by shape memory alloy elements, starting from the constructive-functional scheme. The 3D models of the device components are shown first, and then the device assembly in two variants: in open position, and in closed position. Shape-memory alloy ; Constructive ; Robot ; Robotic arm ; Grippers Shape Memory Alloy Transformations ; Modular Robots and Swarm Intelligence ; Soft Robotics and Applications
Authors: Tianqi Bai | Tao Zhang | Songfeng Pei | R. Le Van Mao | Fachen Liu | Jiade Li | Ruochen Shi | Jinlong Du | Lede Xian | Wencai Ren | Zhongfan Liu | Peng Gao
Source: Research Square (preprint, 2025-08-25)
Field: Materials Science — Materials Chemistry

Direct thermal resistance measurment of a single ripplocation in graphite Graphite ; Materials science ; Resistance (ecology) ; Thermal ; Composite material ; Physics ; Thermodynamics ; Biology Thermal properties of materials ; Graphite, nuclear technology, radiation studies ; Graphene research and applications
Authors: Laura Quintana | Gonzalo Rosa | Javier Santana-Nuñez | Miguel Chavarrías | Samuel Ortega | Jaime Sancho | Himar Fabelo | Eduardo Juárez | Gustavo M. Callicó
Source: IEEE Transactions on Instrumentation and Measurement (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Roadmap for the Characterization and Validation of Hyperspectral Microscopic Systems Hyperspectral (HS) imaging (HSI) is a powerful image technique that allows capturing spatial and spectral information, being able to characterize materials, tissues, and elements in a non-invasive manner. HSI technology is well established at the macroscopic level, but there are still technical challenges to overcome before it can be applied to the microscopic world, such as the lack of standardized characterization methodologies to HS microscopic systems that allow the correct data acquisition as well as ensure the repeatability of the experiments. In this work, we propose a comprehensive roadmap for characterizing and validating such systems, integrating essential parameters highlighted in the current state of the art. Furthermore, we provide a list of the materials needed for their characterization and testing the methodology on two different HS microscopic systems chosen as representative of common configurations in the field, where a HS camera is integrated into a bright-field microscope. Our proposed roadmap assesses the following parameters: dynamic range, noise quantification, pixel size, spatial frequency response, spatial scanning accuracy, spatial repeatability, flat-field correction, tone transfer, and spectral sensitivity. We address the challenge of unifying these parameters into a unified and standardized roadmap. All data used to characterize both systems have been captured by the authors. In summary, this comprehensive analysis provides a guideline for the scientific community to develop and characterize HS microscopic systems to ensure reliability, efficiency, and accuracy. Hyperspectral imaging ; Characterization (materials science) ; Remote sensing ; Computer science ; Environmental science ; Geology ; Materials science ; Nanotechnology Spectroscopy and Chemometric Analyses ; Optical Imaging and Spectroscopy Techniques ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Yang Ke | Xiaoyu Li | Yingyu Liang | Zhenmei Shi | Zhao Song
Source: arXiv (Cornell University) (preprint, 2025-01-08)
Field: Materials Science — Materials Chemistry

Circuit Complexity Bounds for Visual Autoregressive Model Understanding the expressive ability of a specific model is essential for grasping its capacity limitations. Recently, several studies have established circuit complexity bounds for Transformer architecture. Besides, the Visual AutoRegressive (VAR) model has risen to be a prominent method in the field of image generation, outperforming previous techniques, such as Diffusion Transformers, in generating high-quality images. We investigate the circuit complexity of the VAR model and establish a bound in this study. Our primary result demonstrates that the VAR model is equivalent to a simulation by a uniform $\mathsf{TC}^0$ threshold circuit with hidden dimension $d \leq O(n)$ and $\mathrm{poly}(n)$ precision. This is the first study to rigorously highlight the limitations in the expressive power of VAR models despite their impressive performance. We believe our findings will offer valuable insights into the inherent constraints of these models and guide the development of more efficient and expressive architectures in the future. Autoregressive model ; STAR model ; Computer science ; Econometrics ; Theoretical computer science ; Autoregressive integrated moving average ; Mathematics ; Machine learning ; Time series Machine Learning in Materials Science ; Machine Learning and Data Classification ; Industrial Vision Systems and Defect Detection
Authors: Raju Lampande | Jon‐Paul DesOrmeaux | Adrian Pizano | Urcan Guler | John W. Hamer | Noel C. Giebink
Source: Advanced Science (article, 2025-11-09)
Field: Materials Science — Materials Chemistry

Positive Feedback Organic‐on‐Silicon Upconversion Devices Positive feedback upconversion devices exhibit optoelectronic bistability and large photon-to-photon gain that can benefit applications ranging from night vision to neuromorphic image classification. Here, a hybrid device architecture is introduced that integrates a tandem organic light-emitting diode (OLED) on top of an organic/Si heterojunction photodiode. These devices respond to near infrared light at wavelengths up λ ≈ 1.1 µm and exhibit a peak photon-to-photon upconversion gain of 9x when triggered with input light intensity <1 µW cm<sup>-2</sup>. It is found that minimizing the capacitance of the photodiode is important for maximizing the upconversion response speed of the device, and that electrical crosstalk in pixelated devices must be overcome to prevent cascading effects where one activated pixel triggers many others. These results mark an important step for optoelectronic upconverters that can be integrated with Si microelectronics, and they establish a path to extend the responsivity further into the near infrared by moving to other inorganic semiconductor platforms such as Ge or InGaAs. Photon upconversion ; Responsivity ; Photodiode ; Photodetector ; Diode ; Heterojunction ; Capacitance ; Semiconductor ; Bistability ; Infrared Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Organic Electronics and Photovoltaics
Authors: Xiaochuan Luo | Rongnian Tang | Chuang Li | Cheng Qian
Source: Remote Sensing (article, 2025-08-27)
Field: Chemistry — Analytical Chemistry

Diagnosis of Potassium Content in Rubber Leaves Based on Spatial–Spectral Feature Fusion at the Leaf Scale Hyperspectral imaging (HSI) technology has attracted extensive attention in the field of nutrient diagnosis for rubber leaves. However, the mainstream method of extracting leaf average spectra ignores the leaf spatial information in hyperspectral imaging and dilutes the response characteristics exhibited by nutrient-sensitive local areas of leaves, thereby limiting the accuracy of modeling. This study proposes a spatial–spectral feature fusion method based on leaf-scale sub-region segmentation. It introduces a clustering algorithm to divide leaf pixel spectra into several subclasses, and segments sub-regions on the leaf surface based on clustering results. By optimizing the modeling contribution weights of leaf sub-regions, it improves the modeling and generalization accuracy of potassium diagnosis for rubber leaves. Experiments have been carried out to verify the proposed method, which is based on spatial–spectral feature fusion to outperform those of average spectral modeling. Specifically, after pixel-level MSC preprocessing, when the spectra of rubber leaf pixel regions were clustered into nine subsets, the diagnostic accuracy of potassium content in rubber leaves reached 0.97, which is better than the 0.87 achieved by average spectral modeling. Additionally, precision, macro-F1, and macro-recall all reached 0.97, which is superior to the results of average spectral modeling. Moreover, the proposed method is also superior to the spatial–spectral feature fusion method that integrates texture features. The visualization results of leaf sub-region weights showed that strengthening the modeling contribution of leaf edge regions is conducive to improving the diagnostic accuracy of potassium in rubber leaves, which is consistent with the response pattern of leaves to potassium. Hyperspectral imaging ; Artificial intelligence ; Pattern recognition (psychology) ; Pixel ; Segmentation ; Feature (linguistics) ; Computer science ; Fusion ; Remote sensing ; Biological system ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Hassan Abu Hashish | Soliman Sharaf | Z. S. Abdel-Rehim | Mahmoud Elbayoumi | Helmy M. El Zoghby | Amal E. M. Elnaggar
Source: Research Square (preprint, 2025-09-12)
Field: Materials Science — Materials Chemistry

Experimental Validation and Advanced Intelligent Control of a Solar-Driven Thermoelectric Cooling System MATLAB/Simulink Modeling, and Simulation Thermoelectric cooling ; Renewable energy ; Photovoltaic system ; Thermoelectric generator ; Thermoelectric effect ; Scalability ; Efficient energy use ; Refrigeration Advanced Thermoelectric Materials and Devices ; Refrigeration and Air Conditioning Technologies ; Heat Transfer and Optimization
Authors: Li‐Qun Chen | Hongxiu Wang | Dongzhi Wu | Jingqin Su | Songsheng Chen | Dongdong Chen | Tao Zhang | Wenhui He
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-09-29)
Field: Chemistry — Organic Chemistry

Network pharmacology, bioinformatics, molecular docking and experimental verification of the mechanism of HXZTS treatment on osteoarthritis synovium Abstract Background Osteoarthritis (OA) is a prevalent joint disease characterized by synovial inflammation and synovial fibroblast dysfunction. Elucidating the molecular mechanisms underlying synovial tissue impairment in OA is crucial for developing targeted therapeutic interventions. Huoxuezhitongsan (HXZTS), a traditional Chinese medicine formula, has shown promising therapeutic effects in treating OA, but its mechanisms of action on synovial tissue remain unclear. Method This study integrated network pharmacology, bioinformatics analysis, molecular docking, and experimental validation to elucidate the potential targets, pathways, and mechanisms by which HXZTS exert therapeutic effects on osteoarthritic synovial tissue. Results Network analysis identified 21 active ingredients and 111 common targets of HXZTS for OA, with key compounds like quercetin, β-sitosterol, and stigmasterol. Enrichment analysis revealed HXZTS modulates inflammation through TNF, IL-17, and MAPK signaling pathways, oxidative stress, apoptosis, angiogenesis, and metabolic regulation. Molecular docking demonstrated strong binding affinities of HXZTS compounds with targets like AKT1, TNF, TP53, IL6, and VEGFA. RT-qPCR validation confirmed the downregulation of inflammatory mediators like IL6 and PTGS2, alongside the upregulation of genes associated with potential cartilage repair effects, such as MMP1 and MMP3, upon HXZTS treatment. Conclusions HXZTS exerts therapeutic effects on osteoarthritic synovial tissue through a multifaceted mechanism involving modulation of inflammation, oxidative stress, apoptosis, angiogenesis, and metabolic regulation. These findings provide insights into the potential application of HXZTS as a complementary or alternative therapeutic approach for managing OA and a foundation for developing novel targeted therapies. Osteoarthritis ; Inflammation ; Downregulation and upregulation ; Mechanism of action ; Mechanism (biology) ; Cartilage ; Arthritis ; Signal transduction Synthesis and biological activity ; Inflammatory mediators and NSAID effects ; Synthesis and Reactivity of Sulfur-Containing Compounds
Authors: Shilpa Gaikwad | Sonali Kothari
Source: International Journal of Environmental Sciences (article, 2025-09-08)
Field: Chemistry — Analytical Chemistry

A Handheld Device for Apple Ripeness and Sweetness Grading with Convolutional Neural Network This research describes a handheld system developed for capturing multispectral images of apple fruit to evaluate quality based on ripeness and sweetness. The device consists of a 7-inch touchscreen display powered by a Raspberry Pi 5 and a rechargeable power bank, offering flexibility for field operation. A USB digital microscope camera with 1000× magnification is used to acquire detailed images. Multispectral data is captured using a Digitek lighting device, which provides sequential illumination in six color wavelengths: red, yellow, green, cyan, blue, and magenta. Image capture is automated through Python-based code that controls the lighting sequence and image acquisition. To grade apples by ripeness, a DenseNet-121 convolutional neural network was trained on the collected images, achieving an accuracy of 73.77%. For grading by sweetness, a multi-architecture approach, evaluating both custom convolutional neural networks (CNNs) and transfer learning models based on pre-trained VGG16, ResNet50, and EfficientNetB0 architectures fine-tuned on ImageNet weights was employed, reaching an accuracy of 66.22%. The results demonstrate the feasibility of using low-cost, handheld device paired with deep learning techniques for non-destructive fruit quality evaluation. The system holds promise for on-site grading in agricultural environments, reducing the dependence on hard-to-use and costly laboratory equipment. Mobile device ; Convolutional neural network ; USB ; Artificial neural network ; Digital camera ; Ripeness ; Touchscreen Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Smart Agriculture and AI