jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shan Qin | Chenghao Yu | Yanghao Jin | Gaoyue Zhang | Wei Xu | Ao Wang | Mengmeng Fan | Kang Sun | Shule Wang
Source: Applied Sciences (article, 2025-06-24)
Field: Materials Science — Polymers and Plastics

Assessing the Techno-Economic Feasibility of Bamboo Residue-Derived Hard Carbon Bamboo residues represent an abundant, renewable biomass feedstock that can be converted into hard carbon—an emerging anode material for sodium-ion batteries. This study presents a detailed techno-economic analysis of hard carbon production from bamboo residues across China’s ten most bamboo-rich provinces. Regional feedstock availability was estimated from provincial production statistics, while average transportation distances were derived using a square-root-area-based approximation method. The process includes hydrothermal pretreatment, acid washing, carbonization, graphitization, and ball milling. Material and energy inputs were estimated for each stage, and both capital and operating expenses were evaluated using a discounted cash flow model assuming a 15% internal rate of return. The resulting minimum selling price of bamboo-derived hard carbon ranges from 14.47 to 18.15 CNY/kg. Assuming 10% of bamboo residues can be feasibly collected and processed, these ten provinces could collectively support an annual hard carbon production capacity of approximately 1.04 million tons. The results demonstrate that bamboo residues are a strategically distributed and underutilized resource for producing cost-competitive hard carbon at scale, particularly in provinces with existing bamboo industries and supply chains. Bamboo ; Residue (chemistry) ; Environmental science ; Pulp and paper industry ; Materials science ; Chemistry ; Engineering ; Composite material ; Organic chemistry Natural Fiber Reinforced Composites
Authors: Giovanni di Sarra | Barbara Bravi | Yasser Roudi
Source: Europhysics Letters (EPL) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

The unbearable lightness of restricted Boltzmann machines: Theoretical insights and biological applications Abstract Restricted Boltzmann machines are simple yet powerful neural networks. They can be used for learning structure in data, and are used as a building block of more complex neural architectures. At the same time, their simplicity makes them easy to use, amenable to theoretical analysis, yielding interpretable models in applications. Here, we focus on reviewing the role that the activation functions, describing the input-output relationship of single neurons in RBM, play in the functionality of these models. We discuss recent theoretical results on the benefits and limitations of different activation functions. We also review applications to biological data analysis, namely neural data analysis, where RBM units are mostly taken to have sigmoid activation functions and binary units, to protein data analysis and immunology where non-binary units and non-sigmoid activation functions have recently been shown to yield important insights into the data. Finally, we discuss open problems addressing which can shed light on broader issues in neural network research. Sigmoid function ; Computer science ; Artificial neural network ; Artificial intelligence ; Boltzmann machine ; Simple (philosophy) ; Block (permutation group theory) ; Binary number ; Simplicity ; Restricted Boltzmann machine ; Focus (optics) ; Activation function ; Machine learning ; Theoretical computer science ; Mathematics ; Arithmetic Machine Learning in Materials Science ; Extracellular vesicles in disease ; Advanced Memory and Neural Computing
Authors: Wei Xu | Yanlan Zhu | Xiaolin Zhou | Haoyue Guo | Jingxin Wang | Ruiqi Zhu | Z. W. Hu | Wei Ma | Xing Ma | Xiaojian Li | Xiaomin Xu
Source: Journal of Materials Chemistry C (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

High-density, ultraflexible organic electrochemical transistor array for brain activity mapping Scalable high-density OECT arrays are designed to precisely map and record electrocorticogram signals, facilitating high-resolution brain activity monitoring. Materials science ; Electrochemistry ; Transistor ; Nanotechnology ; Optoelectronics ; Electrode ; Physical chemistry ; Electrical engineering ; Voltage Conducting polymers and applications ; Neuroscience and Neural Engineering ; EEG and Brain-Computer Interfaces
Authors: Rudiati Evi Masithoh | Reza Adhitama Putra Hernanda | Muhammad Fahri Reza Pahlawan | Juntae Kim | Hanim Zuhrotul Amanah | Byoung–Kwan Cho
Source: Optics (article, 2025-01-03)
Field: Chemistry — Analytical Chemistry

Shortwave near Infrared–Hyperspectral Imaging Spectra to Detect Pork Adulteration in Beef Using Partial Least Square Regression Coupled with VIP Wavelength Selections Method Pork adulteration detection in beef is important due to health, economic, and religious concerns. This study explored the use of a Shortwave Near Infrared–Hyperspectral Imaging (SWNIR–HSI) system which captured spectral data across 894–2504 nm to detect adulteration of pork in beef. In this study, minced pork in various concentrations ranging from 0–50% (w/w) were added to pure minced beef. Spectra obtained from the SWNIR–HSI were used to develop a partial least square regression (PLSR) model. The study compared the PLSR results between full wavelengths (variables) and selected wavelengths obtained via the variable importance in projection (VIP) method. The best results from the full-wavelength PLSR model yielded a prediction accuracy (R2P) of 0.940 and a standard error of prediction (SEP) of 4.633%, while using VIP-selected wavelengths improved performance, with R2P of 0.955 and SEP of 3.811%. The study demonstrates the potency of SWNIR–HIS, particularly with selected wavelengths, as an effective and nondestructive tool for accurately predicting pork adulteration levels in beef. Hyperspectral imaging ; Shortwave ; Wavelength ; Remote sensing ; Partial least squares regression ; Near-infrared spectroscopy ; Analytical Chemistry (journal) ; Mathematics ; Materials science ; Chemistry ; Optics ; Statistics ; Chromatography ; Physics ; Optoelectronics ; Geology ; Radiative transfer Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Qiucheng Yang | Jinlong Yang | Yuhao Hu | Xiaopeng Niu | Zhenda Liu | Jian Zou | Junchang Guo | Hao Xiong | Xingshi Gu | Yang Li | Fanfei Yu | Shun‐Peng Zhu | Ming Ye | Xian Yi | Xu Deng
Source: National Science Review (article, 2025-01-13)
Field: Materials Science — Surfaces, Coatings and Films

A skin-inspired durable de-icing surface with boosting interfacial cracks Fracture-based interfacial breakage has shown promise in efficiently removing ice accretion. Here, intrigued by the response of human skin to stress-induced deformation, we present a strategy to design tough-skin de-icing surfaces (TSDSs) that actively manipulate crack-induced ice-substrate interfacial breakage during ice removal. This design leverages the surface instability of thin films to generate extensive wrinkling at the ice-substrate interface, which serves as crack initiation sites. We demonstrate efficient ice shedding by creating wrinkles at two length scales: macro-wrinkles for actively initiating the cracks at the rim of the ice and micro-wrinkles for further promoting the stress concentration at the ice-substrate interface. The TSDS (<i>τ</i> < 10 kPa) displays excellent durability and weather resistance, achieving a large-area ice-self-shedding effect solely through gravity. The universality of the proposed mechanism is verified on multiple materials and potential applications. This design concept offers valuable insights into the creation of durable de-icing materials with enhanced ice-shedding properties. Boosting (machine learning) ; Icing ; Materials science ; Composite material ; Polymer science ; Computer science ; Physics ; Artificial intelligence ; Meteorology Surface Modification and Superhydrophobicity ; Advanced Materials and Mechanics ; Interactive and Immersive Displays
Authors: Chin-Ying Yang | Yanci Zhang | Yuxuan Hou
Source: Botanical studies (article, 2025-01-26)
Field: Chemistry — Analytical Chemistry

Assessing water status in rice plants in water-deficient environments using thermal imaging Our study demonstrate that infrared thermography can effectively detect early signs of water stress in rice, aiding farmers in irrigation planning and enabling precise management and optimal utilization of water resources. Canopy ; Irrigation ; Water content ; Thermography ; Agriculture ; Environmental science ; Water use ; Agronomy ; Biology ; Population ; Soil water ; Crop ; Botany ; Soil science ; Ecology ; Infrared Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Uyen Tu Lieu | Natsuhiko Yoshinaga
Source: Soft Matter (article, 2025-01-01)
Field: Materials Science — Biomaterials

Dynamic control of self-assembly of quasicrystalline structures through reinforcement learning We propose reinforcement learning to control the dynamical self-assembly of a dodecagonal quasicrystal (DDQC) from patchy particles. Patchy particles undergo anisotropic interactions with other particles and form DDQCs. However, their structures in steady states are significantly influenced by the kinetic pathways of their structural formation. We estimate the best temperature control policy using the Q-learning method and demonstrate its effectiveness in generating DDQCs with few defects. It is found that reinforcement learning autonomously discovers a characteristic temperature at which structural fluctuations enhance the chance of forming a globally stable state. The estimated policy guides the system toward the characteristic temperature to assist the formation of DDQCs. We also illustrate the performance of RL when the target is metastable or unstable. Reinforcement learning ; Reinforcement ; Quasicrystal ; Self-assembly ; Control (management) ; Materials science ; Nanotechnology ; Computer science ; Chemistry ; Artificial intelligence ; Crystallography ; Composite material Supramolecular Self-Assembly in Materials ; Quasicrystal Structures and Properties ; Advanced Materials and Mechanics
Authors: Sardam Salam Shkur Shkur | Azad A. Mohammed | Soran Abdrahman Ahmad | Hersh F. Mahmood
Source: Smart and Green Materials (review, 2025-08-06)
Field: Materials Science — Polymers and Plastics

Effects of Using Natural Fibers on the Fresh and Mechanical Properties of Concrete: A Comprehensive Review Most of the materials used in the construction sector are concrete, which has some disadvantages and weaknesses, such as low ductility and tensile strength. Many studies have been conducted to improve these properties, including the use of natural fibers. This article reviews previous experimental work on the use of different types of natural fibers in concrete at varying rates, highlighting their effects on both the fresh and mechanical properties of concrete. Based on the reviewed experimental data, relationships between concrete properties and the rate of fiber usage have been established. The effect of natural fibers on the slump value varies depending on the water absorption ability of the fibers. The slump decreases as the rate of natural fiber usage increases. The effect of natural fibers on compressive strength depends on fiber length and the water absorption ability of the fibers. As fiber length increases, the voids in the concrete increase, leading to a decrease in compressive strength. The effect of natural fibers on the flexural strength of concrete is influenced by fiber length and the elongation ability of the fibers. In terms of tensile strength, it was found that using sisal fiber in concrete at 0.5% increases the tensile strength by up to 30% compared to the control mix. Using jute fiber in concrete at 1.5% increases the tensile strength by up to 110% compared to the control mix. Using coir fiber in concrete at 1.5% increases the tensile strength by up to 60% compared to the control mix. Natural (archaeology) ; Materials science ; Composite material ; Construction engineering ; Architectural engineering ; Engineering ; Geology Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Innovations in Concrete and Construction Materials
Authors: Yueyi Zhang | Feng‐Yuan Han | Yang Xiao | Ziwen Zhang | Jianfeng Yang | Yu‐Lu Lei | Fei Gao | Hongsheng Chen | Chao‐Hai Du
Source: Light Science & Applications (article, 2025-09-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Missing harmonic dynamics in generalized Snell’s law: revealing full-channel characteristics of gradient metasurfaces The conventional generalized Snell's law (GSL), derived from classical laws of optical reflection and refraction, governs wavefront manipulation via phase gradients but neglects higher-order spatial harmonics inherently excited by the mutual coupling among meta-atoms on a metasurface. Here, we introduce a spatial harmonic-expanded GSL (SH-GSL) framework by unifying phase-gradient control with Floquet periodicity, establishing spatial harmonics as independent degrees of freedom rather than conventional parasitic disturbances. The SH-GSL framework rigorously identifies the intrinsic harmonic dynamics inherent to metasurfaces, which is a critical feature absent in GSL. Furthermore, this framework further reveals that all gradient-phase metasurfaces inherently function as multichannel platforms due to full spatial harmonics, with this multifunctionality rooted in nonlocal Floquet-Bloch modal interactions. Experimental validation demonstrates: abnormal spatial-harmonic reflection with angular precision ( < 5° deviation), multi-beam splitting (dual/quad configurations) via the relationship between specific harmonics and compensation wave vectors, and a perfect three-channel retroreflector achieving up to 99% efficiency, where parasitic harmonics are confined to near-field plasmonic regimes. This framework establishes a deterministic Floquet-engineered momentum compensation mechanism to simultaneously activate target harmonic channels while confining parasitic harmonics to near-field plasmonic regimes. Experimental validation confirms the framework's accuracy and scalability, bridging momentum-space physics with practical meta-plasmon systems. This work redefines metasurface engineering paradigms, unlocking advancements in ultra-dense beamforming, sensing, and meta-photonics through harmonic-division multiplexing. Harmonics ; Wavefront ; Harmonic ; Floquet theory ; Coupling (piping) ; Modal ; Topology (electrical circuits) ; High harmonic generation Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Plasmonic and Surface Plasmon Research
Authors: Avesahemad Husainy | Suresh M. Sawant | Sonali Kale | A. Amouri | Habib M. Pathan
Source: ES General (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Nano-Enhanced Phase Change Materials: A Novel Approach to Sustainable Refrigeration and Thermal Energy Storage The utilization of phase change materials (PCMs) (eutectic mixture) integrated with graphene nanoparticles enhanced the thermal performance and mitigated temperature fluctuations in refrigeration systems during power outages, giving promising enhancements in system performance, including improved temperature regulation, reduced energy consumption, and minimized temperature fluctuations during operation. Thermal energy storage ; Refrigeration ; Phase change ; Materials science ; Phase-change material ; Nano- ; Thermal ; Energy storage ; Process engineering ; Nanotechnology ; Engineering physics ; Mechanical engineering ; Composite material ; Thermodynamics ; Engineering ; Physics Advanced Thermoelectric Materials and Devices ; Phase Change Materials Research
Authors: Cem Baltacıoğlu | Mehmet Yetişen | Hande Baltacıoğlu | Aberham Hailu Feyissa
Source: Scientific Reports (article, 2025-08-01)
Field: Chemistry — Analytical Chemistry

Determination of physicochemical properties and bioactive compounds of dried apples by FTIR spectroscopy and multispectral imaging This study examines the impact of puffing drying on the physicochemical and structural properties of apple slices using multispectral imaging (UV-NIR spectroscopy), FTIR spectroscopy, and chemometric analysis. Puffing drying significantly impacted apple slice quality, reducing lightness (L* from 75.00 to 43.62) and moisture content (from 10.34 to 7.41%), while increasing redness (a* from 5.41 to 12.32), acidity (from 0.37 to 0.49%), and hardness (from 422.06 to 2967.06). Despite enhanced texture, total phenolic content decreased from 3650.03 to 3307.28 mg GAE/kg, indicating some loss of nutritional compounds during processing. FTIR analysis revealed structural modifications in carbohydrates and lipids, while total phenolic content decreased, indicating thermal degradation. PCA distinguished control and puffed-dried samples, with the first principal component accounting for 98.5% of total variability. PLS regression effectively predicted lightness, redness, and hardness using FTIR spectral data. Multispectral imaging confirmed notable color and texture differences, with reduced lightness and increased surface heterogeneity. PCA of UV-NIR spectra further separated the sample groups, confirming compositional changes. These findings highlighted the potential of multispectral imaging as a non-destructive tool for monitoring drying-induced transformations. Fourier transform infrared spectroscopy ; Multispectral image ; Chemistry ; Spectroscopy ; Chemical engineering ; Remote sensing ; Geology Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Food Drying and Modeling
Authors: Igor Petrović | F. Vučajnk | Valentina Španić
Source: AgriEngineering (article, 2025-02-03)
Field: Chemistry — Analytical Chemistry

Detection of Fusarium Head Blight in Wheat Using NDVI from Multispectral UAS Measurements and Its Correlation with DON Contamination Fusarium head blight (FHB) is a serious fungal disease of wheat and other small cereal grains, significantly reducing grain yield and producing mycotoxins that affect food safety. There is a need for disease detection technologies to determine the right time to apply fungicides, as FHB infection begins before visible symptoms appear. Using multispectral remote sensing by an unmanned aircraft system (UAS), wheat plants were observed under field conditions infested with FHB and simultaneously protected with fungicides sprayed with four different types of nozzles, as well as corresponding control plots infested with FHB only. The results showed that the levels of deoxynivalenol (DON) differed significantly between the five treatments, indicating that the control had the highest DON concentration as no fungicide treatment was applied. This study revealed that the assessment of the normalized difference vegetation index (NDVI) after FHB infection could be useful for predicting DON accumulation in wheat, as a significant negative correlation between DON and NDVI values was measured 24 days after anthesis. The decreasing NDVI values at the end of the growth cycle were expected due to senescence and yellowing of the wheat spikes and leaves. Therefore, significant differences in the NDVI were observed between three measurement points on the 13th, 24th, and 45th day after anthesis. Additionally, the green normalized difference vegetation index (GNDVI) and normalized difference red-edge index (NDRE) were in significant positive correlation with the NDVI at 24th day after anthesis. The use of appropriate measurement points for the vegetation indices can offer the decisive advantage of enabling the evaluation of very large breeding trials or farmers’ fields where the timing of fungicide application is particularly important. Fusarium ; Multispectral image ; Contamination ; Normalized Difference Vegetation Index ; Environmental science ; Agronomy ; Biology ; Remote sensing ; Horticulture ; Geography ; Ecology Spectroscopy and Chemometric Analyses ; Mycotoxins in Agriculture and Food ; Remote Sensing in Agriculture
Authors: B. Lee | Yeseul Kim | Hyun-Yong Lee | Chang Mook Kang
Source: Sensors (article, 2025-01-24)
Field: Materials Science — Materials Chemistry

Bidirectional Gated Recurrent Unit Neural Network for Fault Diagnosis and Rapid Maintenance in Medium-Voltage Direct Current Systems With the growing penetration of renewable energy sources, ensuring the stability and reliability of Medium-Voltage Direct Current (MVDC) systems has become more critical than ever. A single fault in MVDC systems can cause significant disturbances, necessitating rapid and precise diagnostics to prevent equipment damage and maintain continuous power supply. In this work, we present a Bidirectional Gated Recurrent Unit (Bi-GRU) model that both classifies and locates MVDC faults. By capturing the temporal behavior of voltage signals, the Bi-GRU framework surpasses traditional algorithms such as Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Furthermore, the proposed approach addresses multiple fault scenarios including PTP (Pole-to-Pole), PPTG (Positive Pole-to-Ground), and NPTG (Negative Pole-to-Ground) while preserving real-time diagnostic capabilities. In extensive tests, our model achieves an overall accuracy of 95.54% and an average fault detection time below 1.3 ms, meeting real-world operational requirements. To assess robustness, sensor noise was artificially introduced to emulate realistic conditions. Despite these challenging inputs, our method consistently maintained high diagnostic accuracy, confirming its practicality and reliability. Consequently, the proposed scheme demonstrates a significant contribution toward improving the safety and dependability of MVDC systems, even under noisy conditions. Dependability ; Robustness (evolution) ; Voltage ; Reliability engineering ; Engineering ; Electric power system ; Computer science ; Fault detection and isolation ; Direct current ; Reliability (semiconductor) ; Artificial neural network ; Real-time computing ; Power (physics) ; Artificial intelligence ; Electrical engineering ; Actuator High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Non-Destructive Testing Techniques
Authors: Brigita Kolčavová Sirková | Veronika Tunáková | Maroš Tunák | Karol Jezik
Source: Heliyon (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetic shielding effectiveness of three-dimensional multilayered interlaced woven fabrics using stainless steel fibers This study explores and discusses the design, the manufacturing and the morphology of three-dimensional (3D) multilayered weft interlaced woven fabrics using stainless steel fibers on the electromagnetic shielding efficiency (SE). Design solutions of 3D multilayered interlaced fabrics in relation to electromagnetic shielding efficiency are still not sufficiently investigated. Moreover, this study aims to analyze the differences in the internal geometry of 3D multilayered weft interlaced fabrics with different number of layers and frequency of connecting points in multilayered woven fabrics on electromagnetic SE. For this study, the input staple yarn 2 × 20 tex (mixed 80 % polyester/20 % stainless steel fibers) is used for production of approximately 28 types of different 3D multilayer weft interlaced woven fabrics (two and three-layer), with connecting points ranging from 0.5 cm × 0.5 cm-5 cm × 5 cm. The comparison of internal fabric microstructures indicates statistically significant differences in relation to SE, and this contribution extends the theoretical guidance of woven fabric construction with the design and production of special 3D multilayered interlaced fabrics with controlled SE. Electromagnetic shielding ; Materials science ; Woven fabric ; Composite material ; Yarn ; Polyester ; Fabric structure ; Layer (electronics) Electromagnetic wave absorption materials ; Textile materials and evaluations ; Mechanical Behavior of Composites