jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Özcan Çataltaş
Source: New Trends in Computer Sciences (article, 2025-06-19)
Field: Chemistry — Analytical Chemistry

Effect of noise reduction on PLSR modeling in near infrared spectroscopy using denoising autoencoder In this study, a deep learning-based denoising autoencoder approach is proposed to increase the robustness of near-infrared spectroscopy data to random noise and improve quantitative modeling accuracy. Artificial Gaussian noise at four different levels (10, 15, 20, and 25 dB) was added to the near-infrared spectra obtained from milk samples to mimic the real measurement conditions. The noisy spectra were denoised by processing with an autoencoder architecture consisting of fully connected layers. The noise removal performance is quantitatively evaluated with both theoretical and measured signal-to-noise ratio values. The results show that the AE model significantly improves the spectral signal quality at all signal-to-noise ratio levels. In particular, at the lowest signal-to-noise ratio level (10 dB), the signal-to-noise ratio value nearly tripled to 29.6 dB with the autoencoder. At all other levels, an average increase of 18-20 dB was observed in the signal-to-noise ratio of the denoised spectra. In the second stage of the study, Partial Least Squares Regression models were built using both the noisy and cleaned spectra and evaluated on the test set with root mean square error and coefficient of determination. The Partial Least Squares Regression models built with the denoised spectra achieved lower root mean square error and higher coefficient of determination values at all signal-to-noise ratio levels. Especially at the 10 dB signal-to-noise ratio level, the coefficient of determination value of the model increased from 0.44 to 0.71, while the root means square error decreased from 0.60 to 0.43. The results show that the deep learning-based AE architecture can effectively reduce random noise in near-infrared spectral data and significantly improve both spectral signal quality and quantitative modeling performance. This approach provides an effective solution to improve model reliability and accuracy in near-infrared spectroscopy analysis. Mean squared error ; Noise reduction ; Partial least squares regression ; Noise (video) ; Signal-to-noise ratio (imaging) ; Gaussian noise ; Root mean square ; Mathematics ; Autoencoder ; Correlation coefficient ; Statistics ; Analytical Chemistry (journal) ; Pattern recognition (psychology) ; Algorithm ; Artificial intelligence ; Chemistry ; Computer science ; Physics ; Artificial neural network Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Luyang Zhang | Lin Chen | Jiankun Wang | Bingyan Wu | Jing Feng
Source: Journal of the American Ceramic Society (article, 2025-08-07)
Field: Materials Science — Ceramics and Composites

Simultaneously reduced thermal expansion coefficients and conductivity of SiO<sub>2</sub>–AlTaO<sub>4</sub> oxides with the enhanced toughness Abstract Directional optimization of properties is of significant interest for various protective coatings materials. This study investigates the effects of SiO 2 doping on AlTaO 4 ceramics as environmental barrier coatings (EBCs), and the changes in crystal structure and comprehensive properties are thoroughly investigated. The x mol% SiO 2 –AlTaO 4 (SATO, x = 0, 3, 6, 9, 12, 15) ceramics are fabricated via a solid‐state sintering process, and their structures, as well as thermo‐mechanical properties, are systematically analyzed. The results reveal that SiO 2 doping introduces point defects and lattice distortions, which significantly reduce the thermal conductivity by 44% at 900°C, and effectively suppresses the increase in thermal conductivity at elevated temperatures. Furthermore, the fracture toughness is improved from 3.01 to 3.38 MPa·m 1/2 , demonstrating superior mechanical properties compared with other EBC materials. The thermal expansion coefficients (TECs) are reduced to 5.71 × 10 −6 K −1 at 1200°C, which match with those of SiC ceramic matrix composites (TECs = 5.50 × 10 −6 K −1 ). These findings highlight that SATO ceramics are promising candidates as EBC materials, and SiO 2 doping can further advance its high‐temperature applications. Thermal expansion ; Thermal conductivity ; Materials science ; Toughness ; Composite material ; Conductivity ; Mineralogy ; Chemistry ; Physical chemistry Advanced ceramic materials synthesis ; Nuclear materials and radiation effects ; Microwave Dielectric Ceramics Synthesis
Authors: Yifan Jiang | Shang Jin | Yueyue Cai | Shiyang Liu | Zhongxing Liao | Jie Pang | Yong He | Xuan Wei
Source: Agriculture (article, 2025-07-18)
Field: Chemistry — Analytical Chemistry

The Fusion of Focused Spectral and Image Texture Features: A New Exploration of the Nondestructive Detection of Degeneration Degree in Pleurotus geesteranus The degradation of edible fungi can lead to a decrease in cultivation yield and economic losses. In this study, a nondestructive detection method for strain degradation based on the fusion of hyperspectral technology and image texture features is presented. Hyperspectral and microscopic image data were acquired from Pleurotus geesteranus strains exhibiting varying degrees of degradation, followed by preprocessing using Savitzky–Golay smoothing (SG), multivariate scattering correction (MSC), and standard normal variate transformation (SNV). Spectral features were extracted by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA), while the texture features were derived using gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) models. The spectral and texture features were then fused and used to construct a classification model based on convolutional neural networks (CNN). The results showed that combining hyperspectral and image texture features significantly improved the classification accuracy. Among the tested models, the CARS + LBP-CNN configuration achieved the best performance, with an overall accuracy of 95.6% and a kappa coefficient of 0.96. This approach provides a new technical solution for the nondestructive detection of strain degradation in Pleurotus geesteranus. Pleurotus ; Texture (cosmology) ; Degree (music) ; Pattern recognition (psychology) ; Artificial intelligence ; Computer vision ; Computer science ; Botany ; Biology ; Image (mathematics) ; Mushroom ; Physics ; Acoustics Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection
Authors: Prajakta Subhedar | Divya Padmanabhan | Richa Agrawal
Source: Journal of Research Updates in Polymer Science (article, 2025-09-24)
Field: Materials Science — Biomaterials

Biomedical Applications of Polycaprolactone (PCL) Composites: Structure, Properties, and Future Prospects Polycaprolactone (PCL) is a semi-crystalline, biodegradable aliphatic polyester that has emerged as a versatile biomaterial for tissue engineering, drug delivery, and regenerative medicine applications due to its exceptional biocompatibility, controlled degradation kinetics (2-4 years in vivo), and FDA approval status for multiple medical devices. Despite these advantages, pure PCL exhibits significant limitations including low mechanical strength (16-24 MPa tensile strength), hydrophobic surface properties (water contact angle 80-90°), and minimal bioactivity, which restrict its clinical utility in load-bearing and cell-interactive applications. To address these shortcomings, researchers have developed PCL-based composite systems by incorporating bioactive ceramics (hydroxyapatite, β-tricalcium phosphate), natural polymers (collagen, chitosan, gelatin), synthetic polymers (PLA, PLGA), and nanomaterials (carbon nanotubes, graphene oxide) to create multifunctional biomaterials with enhanced properties. This comprehensive review analyzes PCL composite development over the past two decades, emphasizing fabrication techniques including electrospinning, 3D printing, solvent casting, and melt blending, which enable precise control over scaffold architecture and functionality. Comparative analysis with other biodegradable polymers (PGA, PLGA) reveals PCL's unique advantages in long-term applications, with studies demonstrating &gt;90% cell viability, ~65% bone regeneration in animal models, and sustained drug release profiles extending 6-8 weeks. Recent innovations include smart, stimuli-responsive PCL systems for targeted therapy, gene delivery platforms, and bioprinting applications that have advanced from laboratory research to clinical trials, with several PCL-based products (Neurolac®, Osteoplug®) receiving regulatory approval. Current challenges include manufacturing scalability, long-term biocompatibility assessment, and complex regulatory pathways for multi-component systems. Future developments focus on integrating artificial intelligence for scaffold design, 4D printing technologies for dynamic structures, and multidisciplinary approaches combining materials science with precision medicine. This review demonstrates that PCL-based composites represent a transformative class of biomaterials with customizable properties that bridge fundamental research and clinical translation, positioning them at the forefront of next-generation biomedical technologies. Polycaprolactone ; Scaffold ; Biocompatibility ; Tissue engineering ; Drug delivery ; Biomaterial ; Polyester ; 3D bioprinting ; Regenerative medicine Electrospun Nanofibers in Biomedical Applications ; biodegradable polymer synthesis and properties ; Bone Tissue Engineering Materials
Authors: Jie Xu | Changsheng Qin | Hui Wang
Source: Materials (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

C15-Structured Zr-Ti-Fe-Ni-V Alloys for High-Pressure Hydrogen Compression Metal hydride hydrogen compressors (MHHC) offer unique advantages over conventional mechanical compressors in high-pressure hydrogen refueling. In this study, we developed C15-structured Zr-Ti-Fe-Ni-V single-phase alloys for high-pressure hydrogen compression. By designing the alloy compositions-high Ni and low V-and employing a quenching process, the resulting ZrFe<sub>2</sub>-based alloys exhibit reduced hydriding/dehydriding plateau hysteresis and slope, along with a narrow hydrogen solid solution zone. Notably, the Zr<sub>0.8</sub>Ti<sub>0.2</sub>Fe<sub>1.2</sub>Ni<sub>0.7</sub>V<sub>0.1</sub> alloy elevates the hydrogen pressure from 128.3 atm to 334.5 atm within 283-353 K, delivering an effective hydrogen capacity of 1.02 wt.%. Similarly, the Zr<sub>0.9</sub>Ti<sub>0.1</sub>Fe<sub>1.2</sub>Ni<sub>0.7</sub>V<sub>0.1</sub> alloy increases the hydrogen pressure from 60.4 atm to 221.8 atm across 283-363 K, with a capacity of 0.81 wt.%. This work provides a rational strategy for designing ZrFe<sub>2</sub>-based alloys for efficient hydrogen compression and storage applications. Hydrogen ; Alloy ; Hydrogen storage ; Hydride ; Quenching (fluorescence) ; Gas compressor ; Compression (physics) ; Work (physics) Hydrogen Storage and Materials ; Hydrogen embrittlement and corrosion behaviors in metals ; Catalysts for Methane Reforming
Authors: Е. П. Симоненко | А. В. Чаплыгин | Н. П. Симоненко | I. V. Lukomskii | А. С. Лысенков | Ilya A. Nagornov | Kirill A. Barsukovsky | T. L. Simonenko | Artem S. Mokrushin | А. Ф. Колесников | Н. Т. Кузнецов
Source: Journal of Composites Science (article, 2025-12-12)
Field: Materials Science — Ceramics and Composites

Influence of the Ti2AlC Sintering Additive on the Behaviour of ZrB2-SiC Ultra-High Temperature Ceramic in a Subsonic CO2 Plasma Flow The investigation of the behavior of ZrB2-SiC-based ultra-high temperature ceramic (UHTC) materials under high-velocity CO2 plasma flow is of significant importance and relevance for evaluating their prospective use in the exploration of planets such as Venus or Mars. Accordingly, the degradation process of a ZrB2-30 vol.% SiC ceramic composite, fabricated by hot-pressing at 1700 °C with a 15 vol.% Ti2AlC sintering aid, was examined using a high-frequency induction plasmatron. It was found that the modification of the ceramic’s elemental and phase composition during consolidation, resulting from the interaction between ZrB2 and Ti2AlC, leads to the formation of an approximately 400 µm-thick multi-layered oxidation zone following 15 min stepwise thermochemical exposure at surface temperatures reaching up to 1970 °C. This area consists of a lower layer depleted of silicon carbide and an upper layer containing large pores (up to 160–200 µm), where ZrO2 particles are distributed within a silicate melt. SEM analysis revealed that introduction of more refractory titanium and aluminum oxides into the melt upon oxidation, along with liquation within the melt, prevents the complete removal of this sealing melt from the sample surface. This effect remains even after 8 min exposure at an average temperature of ~1960–1970 °C. Ceramic ; Sintering ; Silicon carbide ; Titanium ; Layer (electronics) ; Carbide ; Phase (matter) ; Aluminium ; Dissolution Advanced ceramic materials synthesis ; MXene and MAX Phase Materials ; Intermetallics and Advanced Alloy Properties
Authors: Timur Biktagirov | W. G. Schmidt | Karl Schiller | Michele Capra | Jonah Elias Nitschke | Lasse Sternemann | Mira Sophie Arndt | Giovanni Zamborlini | Anna Isaeva | Mirko Cinchetti
Source: npj 2D Materials and Applications (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Intrinsic defects as a source of n-type conductivity in CrSBr Understanding and controlling native defects is essential for unlocking the full potential of two-dimensional magnetic semiconductors. Here, angle-resolved photoemission spectroscopy (ARPES) and first-principles calculations are used to explore the electronic properties of bulk CrSBr. ARPES measurements reveal clear signatures of conduction band filling in as-grown crystals, indicative of unintentional doping. An analysis of intrinsic defects based on density functional theory (DFT) identifies chromium interstitials (Cri) stabilized between CrSBr layers as the most favorable shallow donors. Bromine-on-sulfur antisites (BrS) and bromine vacancies (VBr) are also found to act as potential donors, albeit with deeper ionization energies. Our results shed light on the origin of unintentional n-type doping of CrSBr and pave the way for new strategies for defect control and electronic property tuning in this van der Waals magnet. Angle-resolved photoemission spectroscopy ; Doping ; van der Waals force ; Density functional theory ; Photoemission spectroscopy ; Conductivity ; Electrical resistivity and conductivity ; Thermal conduction 2D Materials and Applications ; Iron-based superconductors research ; Advanced Photocatalysis Techniques
Authors: Iker Lopetegi | Sergio Fernandez | Gregory L. Plett | M. Scott Trimboli | Unai Iraola
Source: Journal of The Electrochemical Society (preprint, 2025-06-01)
Field: Chemistry — Electrochemistry

Electrode State of Charge and State of Health Estimation with Electrode-Level Equivalent-Circuit Models To prolong battery lifetime and maximize performance, battery management systems (BMSs) need to estimate internal variables that are related to battery aging. For that, accurate battery models that can represent internal variables that are not directly measurable are required. However, most present BMSs use equivalent-circuit models (ECMs or EQMs) for state of charge (SOC) and state of health (SOH) estimation. These models are not able to predict these aging-related variables, and therefore, they cannot be used to limit battery degradation. In this paper, we propose a method for electrode-level SOC (eSOC) and electrode-level SOH (eSOH) estimation using an electrode-level ECM (eECM), which can describe both electrodes’ potentials, giving valuable information for aging prevention. The method produces state of lithiation (SOL) and potential estimates of both electrodes, and updates the eSOH parameters to maintain estimation accuracy through the lifetime of the battery. Furthermore, the eSOH parameter estimates are used to obtain degradation mode information, which could be used to improve state estimation, health diagnosis and prognosis. Electrode ; Computer science ; Chemistry Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Force Microscopy Techniques and Applications
Authors: Hannah Kniesel | Poonam Poonam | Tristan Payer | Tim Bergner | Pedro Hermosilla | Timo Ropinski
Source: Journal of Microscopy (article, 2025-06-28)
Field: Materials Science — Surfaces, Coatings and Films

DeepEM Playground: Bringing deep learning to electron microscopy labs Deep learning (DL) has transformed image analysis, enabling breakthroughs in segmentation, object detection, and classification. However, a gap persists between cutting-edge DL research and its practical adoption in electron microscopy (EM) labs. This is largely due to the inaccessibility of DL methods for EM specialists and the expertise required to interpret model outputs. To bridge this gap, we introduce DeepEM Playground, an interactive, user-friendly platform designed to empower EM researchers - regardless of coding experience - to train, tune, and apply DL models. By providing a guided, hands-on approach, DeepEM Playground enables users to explore the workings of DL in EM, facilitating both first-time engagement and more advanced model customisation. The DeepEM Playground lowers the barrier to entry and fosters a deeper understanding of deep learning, thereby enabling the EM community to integrate AI-driven analysis into their workflows more confidently and effectively. Workflow ; Deep learning ; Computer science ; Segmentation ; Coding (social sciences) ; Bridge (graph theory) ; Human–computer interaction ; Nanotechnology ; Data science ; Artificial intelligence ; Materials science ; Sociology Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Pimpen Pornchaloempong | Sneha Sharma | Thitima Phanomsophon | Panmanas Sirisomboon | Ravipat Lapcharoensuk
Source: Horticulturae (article, 2025-09-02)
Field: Chemistry — Analytical Chemistry

Revealing the Power of Deep Learning in Quality Assessment of Mango and Mangosteen Purée Using NIR Spectral Data The quality control of fruit purée products such as mango and mangosteen is crucial for maintaining consumer satisfaction and meeting industry standards. Traditional destructive techniques for assessing key quality parameters like the soluble solid content (SSC) and titratable acidity (TA) are labor-intensive and time-consuming; prompting the need for rapid, nondestructive alternatives. This study investigated the use of deep learning (DL) models including Simple-CNN, AlexNet, EfficientNetB0, MobileNetV2, and ResNeXt for predicting SSC and TA in mango and mangosteen purée and compared their performance with the conventional chemometric method partial least squares regression (PLSR). Spectral data were preprocessed and evaluated using 10-fold cross-validation. For mango purée, the Simple-CNN model achieved the highest predictive accuracy for both SSC (coefficient of determination of cross-validation (RCV2) = 0.914, root mean square error of cross-validation (RMSECV) = 0.688, the ratio of prediction to deviation of cross-validation (RPDCV) = 3.367) and TA (RCV2 = 0.762, RMSECV = 0.037, RPDCV = 2.864), demonstrating a statistically significant improvement over PLSR. For the mangosteen purée, AlexNet exhibited the best SSC prediction performance (RCV2 = 0.702, RMSECV = 0.471, RPDCV = 1.666), though the RPDCV values (&lt;2.0) indicated limited applicability for precise quantification. TA prediction in mangosteen purée showed low variance in the reference values (standard deviation (SD) = 0.048), which may have restricted model performance. These results highlight the potential of DL for improving NIR-based quality evaluation of fruit purée, while also pointing to the need for further refinement to ensure interpretability, robustness, and practical deployment in industrial quality control. Quality (philosophy) ; Horticulture ; Mathematics ; Environmental science ; Agricultural engineering ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Biology ; Physics ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Piotr Dziurdzia | Piotr Bratek | Ireneusz Brzozowski | Michał Markiewicz
Source: Energies (article, 2025-09-20)
Field: Materials Science — Materials Chemistry

An Electrothermal Model of a Heatsink-Less Thermoelectric Generator in a Thermalization State The paper presents the development and experimental verification of an electrothermal model of a heatsink-less thermoelectric generator (TEG) implemented in the LTspice simulator. The model incorporates key physical phenomena, including the Seebeck effect, the Peltier effect, and Joule heating. It also takes into account a variable convective thermal resistance to the environment that depends on the temperature of the thermoelectric module’s cold side. The model was calibrated using experimental measurements of the open-circuit Seebeck voltage and the output voltage under resistive load connected to the TEC1-12706-SR thermoelectric module (TEM), under controlled temperature gradients. The model’s accuracy was validated by comparing simulation results with measured output voltages and power generated by the TEG for various load resistances and ambient temperatures. The simulations showed good agreement with the experimental data. The analysis and tests also confirmed the existence of an optimal load resistance that maximizes power transfer from the module, which is consistent with the principle of matching the load to the TEG’s internal resistance. We present the comparison between the theoretical model of a TEG and its physical properties. We used the results of the measurements to tailor the model, so finally we were able to achieve consistency of measurements with experiment within 10–17%. The developed model is a useful tool for rapid design and optimization of energy-harvesting systems using TEGs, enabling the integration of these generators into autonomous IoT systems and wearable electronics, without the need for a traditional heatsink. Thermoelectric generator ; Thermoelectric effect ; Thermoelectric cooling ; Voltage ; Joule heating ; Internal resistance ; Joule effect ; Joule (programming language) ; Power (physics) Advanced Thermoelectric Materials and Devices ; Advanced Thermodynamics and Statistical Mechanics ; Thermal Radiation and Cooling Technologies