jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ingo Schmitt | Kay Sowoidnich | Tapashi Gosswami | Bernd Sumpf | Martin Maiwald | Matthias Wolff
Source: Journal of Chemometrics (article, 2025-11-01)
Field: Chemistry — Analytical Chemistry

PCA‐Based Peak Feature Selection for Classification of Spectroscopic Datasets ABSTRACT Reducing feature dimensionality in spectroscopic data is crucial for efficient analysis and classification. Using all available features for classification typically results in an unacceptably high runtime and poor accuracy. Popular feature extraction methods, such as principal component analysis (PCA), linear discriminant analysis (LDA), and autoencoders, reduce feature dimensionality by extracting latent features that can be challenging to interpret. To enable better human interpretation of the classification model, we avoid extraction methods and instead propose applying feature selection methods. In this work, we develop an innovative PCA‐based feature selection method for spectroscopic data, providing an essential subset of the original features. As an important advantage, no prior knowledge about the characteristic signals of the respective target substance is required. In this proof‐of‐concept study, the proposed method is initially characterized using simulated Raman and infrared absorption datasets. From the top five PCA eigenvectors of spectroscopic data, we identify a set of three top peaks each at specific wavenumbers (features). The compact set of selected features is then used for classification tasks applying a decision tree. Based on two well‐defined spectroscopic datasets, our study demonstrates that our new method of PCA‐based peak finding outperforms selected other approaches with regard to interpretability and accuracy. For both investigated datasets, accuracies greater than 97% are achieved. Our approach shows large potential for accurate classification combined with interpretability in further scenarios involving spectroscopic datasets. Interpretability ; Pattern recognition (psychology) ; Feature selection ; Principal component analysis ; Curse of dimensionality ; Linear discriminant analysis ; Feature extraction ; Dimensionality reduction Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: Yeongmin Kim | Sungwook Choung
Source: Economic and Environmental Geology (article, 2025-12-28)
Field: Materials Science — Materials Chemistry

A Review on Synchrotron Radiation Use in Metallic Ore Deposit Research This review introduces recent research trends in the application of synchrotron radiation (SR) analytical techniques to the study of the genesis and formation processes of metallic ore deposits and briefly summarizes the major SR beamlines commonly employed in related studies.The principal synchrotron-based analytical methods utilized in ore deposit research include XANES/EXAFS, XRF/ nano-XRF, XRD, XRF-CT, and in situ high-pressure-high-temperature (HP-HT) XAS.In recent years, the use of synchrotron techniques in ore deposit studies has been steadily increasing, particularly in fields such as investigations of metal dissolution and complex formation in hydrothermal fluids, characterization of fluid inclusions, substitution mechanisms within sulfide minerals, and the role of redox evolution in ore-forming environments.Furthermore, this study highlights the necessity of establishing dedicated beamlines for Earth and environmental sciences in conjunction with the construction of the next-generation synchrotron facility in Ochang, Korea.Such developments are expected to expand the application of synchrotron-based methods in geoscientific research and strengthen the international competitiveness of Korea's Earth and environmental science community. Synchrotron radiation ; Metallography ; Base metal ; Metal ; Welding Graphite, nuclear technology, radiation studies ; Crystallography and Radiation Phenomena ; Radiation Shielding Materials Analysis
Authors: Damien Redureau | John P. Dunbar | Raphaël La Rocca | Axel De Monts De Savasse | Quentin Bastiaens | Virginie Bertrand | Christopher Kune | Mathieu Tiquet | Johann Far | Edwin De Pauw | Michel M. Dugon | Loïc Quinton
Source: Frontiers in Chemistry (article, 2025-09-03)
Field: Chemistry — Spectroscopy

Organ-resolved lipid mapping in Steatoda nobilis spider model using high-resolution mass spectrometry imaging and Kendrick mass defect analysis The noble false widow spider (<i>Steatoda nobilis</i>), a rapidly spreading member of the Theridiidae family, has gained attention for its increasing presence near human habitats and its medical significance due to envenomation reports. Recent studies have revealed that its venom contains α-latrotoxins, toxins also found in <i>Latrodectus</i> (black widows), responsible for latrodectism symptoms. Despite this growing interest, little is known about the lipidome and metabolome of <i>S. nobilis</i>, which could offer insights into its ecological role, dietary metabolism, and chemical communication. In this study, we used Matrix-Assisted Laser Desorption/Ionization Fourier-Transform Ion Cyclotron Resonance (MALDI-FT-ICR) mass spectrometry imaging (MSI) to investigate the whole-body lipid and metabolite distribution in <i>S. nobilis</i>. MSI is a powerful tool that couples molecular analysis with spatial information, enabling detailed visualization of biomolecules in tissues. Applying MSI to arachnids offers a novel approach to explore organ-specific metabolic profiles and identify potentially bioactive or adaptive compounds. One of the major challenges was preserving the spider's fragile internal anatomy during sample preparation. We developed a gelatin-based fixation method to obtain intact histological sections suitable for MSI analysis. This allowed us to clearly distinguish organ-specific lipid and metabolite distributions <i>in situ</i>, including within the silk glands, ovaries, and nervous tissues. A second challenge was managing the vast data generated by MSI, with each image yielding thousands of molecular peaks. To streamline analysis, we employed Kendrick Mass Defect (KMD) plots to classify ions into structural families. This approach enabled us to link specific ions to molecular families and localize them within the spider's body, enhancing our anatomical understanding at the molecular level. This work not only provides foundational insights into <i>S. nobilis</i> biochemistry but also demonstrates the potential of MSI for advancing arachnid lipidomics and uncovering molecules of ecological or biomedical interest. It opens the gates for broader applications of spatial lipidomics in other small biosystems and animals, particularly those previously inaccessible to detailed biochemical analysis. Mass spectrometry ; Mass spectrometry imaging ; Chemistry ; High resolution ; Resolution (logic) ; Chromatography ; Analytical Chemistry (journal) ; Remote sensing ; Computer science ; Geology Mass Spectrometry Techniques and Applications ; Antimicrobial Peptides and Activities ; Venomous Animal Envenomation and Studies
Authors: Muhammad Adam | Danial Yusri | Zaidi Ghani | D Pandey | A Daverey | K Dutta | V Yata | K Arunachalam | T Aragaw | A Alene | S Soni | P Bajpai | J Mittal | C Arora | Y Teymur | F Gzel | A Veeraragavan | R Shanmugavel | N Abraham | D Subramanian | S Pandian | P Deivasigamani | P Kumar | S Sundaraman | M Soosai | A Renita | M Karthikeyan | N Bektenov | O Baigenzhenov | D Venkatesan | A Kumar | D Mani | D Elango | A Priyadharsan | L Al-Humaid | N Al-Dahmash | S Ragupathy | P Jayanthi | Y Ahn | H Ouaddari | B Abbou | I Lebkiri | A Habsaoui | M Ouzzine | R Allah | L Daniel | A Rahman | M Hamushembe | P Kapolo | V Uahengo | S Jonnalagadda | J Chen | J Li | Q Zeng | H Li | F Chen | H Hou | J Lan | Y Kim | J Kim | A Iqajtaoune | M Taibi | H Saufi | B Aouan | L Boudad | Z Wardighi | J Bensalah | A Zarrouk | A Lebkiri | R Hazzaa | M Hussein | G Joshiba | G Rangasamy | P Ngueagni | G Pooja | G Balji | K Alagumalai | H El-Serehy | M Al-Ghouti | D Da'ana | S Miraboutalebi | S Nikouzad | M Peydayesh | N Allahgholi | L Vafajoo | G Mckay | T Handayani | P Ramadhani | R Zein | J Fu | Z Chen | M Wang | S Liu | J Zhang | R Han | Q Xu | H Shin
Source: Malaysian Journal of Chemistry (article, 2025-06-13)
Field: Chemistry — Analytical Chemistry

Adsorptive Removal of Methylene Blue using Activated Carbon Derived from Waste Coffee Grounds In this work, activated carbon derived from waste coffee grounds (CG -AC) was synthesized using zinc chloride (ZnCl2) as the activating agent.The CG-AC was then employed as an adsorbent for the removal of methylene blue (MB) from aqueous solutions.Adsorption isotherm data were evaluated using the Langmuir, Freundlich, and Temkin models.The Langmuir model, with a correlation coefficient of 0.9998, showed the best fit for the adsorption of MB on CG-AC, with the maximum monolayer adsorption capacity calculated at 176 mg/g.Kinetic studies were conducted using pseudo -first-order (PFO), pseudo-second-order (PSO), and intraparticle diffusion (IPD) models.The R values indicated that the PSO model provided a more accurate description of the adsorption kinetics.The plot of qt versus t 1/2 in the IPD model demonstrated multi-linear behavior, suggesting that the adsorption occurred in multiple stages.Thermodynamic analysis was performed at temperatures ranging from 25 to 34 C, where negative G values and positive H values confirmed the spontaneity and endothermic nature of the adsorption process.Additionally, the positive S value indicated a strong affinity of MB molecules for the CG-AC.These results suggest that CG-AC could be a promising adsorbent for treating wastewater containing high levels of dyes. Methylene blue ; Activated carbon ; Waste management ; Coffee grounds ; Environmental science ; Chemistry ; Nuclear chemistry ; Pulp and paper industry ; Business ; Adsorption ; Organic chemistry ; Engineering ; Food science ; Catalysis ; Photocatalysis Dye analysis and toxicity
Authors: Tatiana E. Gorelik | E.V. Gorelik
Source: Acta Crystallographica Section A Foundations and Advances (article, 2025-07-07)
Field: Materials Science — Materials Chemistry

Machine learning for the classification of serial electron diffraction patterns: synthetic data Serial electron crystallography faces a fundamental challenge due to the flat Ewald sphere resulting from the short electron wavelength, leading to limited 3D information in individual patterns. Recently, an algorithm for unit-cell determination from zonal electron diffraction patterns (GM algorithm) [Miehe (1997). Ber. Dtsch. Miner. Ges. Beih. z. Eur. J. Miner. 9, 250; Gorelik et al. (2025). Acta Cryst. A81, 124-136] was introduced in the context of serial electron crystallography. This algorithm requires the extraction of 2D zonal patterns from the complete serial dataset. Here, we present a machine learning approach for pattern sorting and apply it initially to simulated electron diffraction patterns. Sorting ; Electron diffraction ; Diffraction ; Computer science ; Sorting algorithm ; Context (archaeology) ; Electron ; sort ; Algorithm ; Pattern recognition (psychology) ; Crystallography ; Artificial intelligence ; Optics ; Physics ; Chemistry ; Geology ; Information retrieval X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Corrosion Behavior and Inhibition
Authors: Timur Yu. Ivanenko | Elena V. Fomenko | Evgeny V. Morozov | A.N. Matsulev | Maxim A. Lutoshkin | Nicolay P. Shestakov | В. Ф. Шабанов
Source: International Journal of Molecular Sciences (article, 2025-09-14)
Field: Chemistry — Analytical Chemistry

Determination of Spectral Characteristics and Moisture Distribution in Wheat Grains After Sorption, Thermal, and Natural Drying The seed drying process is one of the most important aspects of post-harvest treatment, which determines the quality of the final product, cost accounting, and storage capacity. Sorption drying is of great scientific and practical importance due to its ability to gently remove moisture, which improves seed quality and ensures energy efficiency. In this study, wheat grains with an initial moisture content of 22% were dried to a moisture content of 13% using sorption, thermal, and natural air drying. The seed germination capacity after drying was 97%, 93%, and 95%, respectively. The effect of different drying methods on the morphological characteristics, microstructure, and moisture content of wheat grains was studied using a combination of experimental techniques. ATR-MIR and MAS NMR analysis revealed the biochemical stability of sorption-dried grains and the complete preservation of characteristic protein amide bands, indicating the absence of molecular degradation. Statistically significant differences in wheat grains after thermal and sorption drying were observed in luminescence peak intensities and standard deviation of the main spectral band's half width. The MRI method demonstrated that sorption drying preserves optimal grain tissue microstructure while maintaining proper moisture levels and distribution prior to germination, as well as supporting natural mass transfer processes and moisture distribution evolution during dehydration. Sorption ; Water content ; Moisture ; Mass transfer ; Germination ; Microstructure ; Equilibrium moisture content Spectroscopy and Chemometric Analyses ; Food Drying and Modeling ; Food composition and properties
Authors: Lapo Renai | Viktoriia Turkina | Tobias Hulleman | Alexandros Nikolopoulos | Andrea Gargano | Elvio D. Amato | Massimo Del Bubba | Saer Samanipour
Source: ChemRxiv (preprint, 2025-05-20)
Field: Chemistry — Analytical Chemistry

A Novel Chemical Space Dependent Strategy for Compound Selection in Non-Target LC–HRMS Method Development Using Physicochemical and Structural Data The virtual chemical space of substances, including emerging contaminants relevant to the environment and exposome, is in continuous expansion. The comprehensive measure of specific regions of chemical (sub)space relies on non-targeted analysis (NTA) using liquid chromatography high-resolution mass spectrometry (LC–HRMS). Internal standards are generally used to balance selectivity and sensitivity during NTA sample preparation and method development, assuming linearity between structure and behavior of all chemicals of interest. Since this assumption does not hold for wide and complex chemical subspaces, the actual measured space by NTA methods is consequently narrowed to a reduced set of chemically/structurally similar compounds. To understand the true coverage of the measurable chemical space by NTA methods, we present a data-driven strategy enabling the unbiased sampling of structures for LC–HRMS method development from a vast chemical subspace of interest (e.g., US-EPA CompTox >1 million chemicals). This workflow mines candidate structures maximizing their physicochemical diversity for reliable chemical subspace coverage. Measurable compound lists (MCLs) are effectively sampled using precomputed PubChem physicochemical properties (e.g., molecular weight and XLogP) and predicted mobility and ionization efficiency from molecular fingerprints. This approach significantly advances the selection of heterogeneous structures compatible with LC–HRMS analysis for NTA method development, validation, and method space boundary assessment while preserving the original diversity of the selected chemical space. The MCL sampled from CompTox space exhibited a greater chemical coverage and a broader predicted LC–HRMS applicability compared to common “watch list” contaminants. Chemical space ; Selection (genetic algorithm) ; Space (punctuation) ; Chemistry ; Combinatorial chemistry ; Biochemical engineering ; Computer science ; Biological system ; Data mining ; Chromatography ; Artificial intelligence ; Drug discovery ; Engineering ; Biology ; Biochemistry Analytical chemistry methods development
Authors: Tatsuhito Hasegawa | Kyosuke Fujino | Shunsuke Sakai
Source: Neural Computing and Applications (article, 2025-08-02)
Field: Materials Science — Materials Chemistry

Analytical softmax temperature setting from feature dimensions for model- and domain-robust classification Abstract In deep learning-based classification tasks, the softmax function’s temperature parameter T critically influences the output distribution and overall performance. This study presents a novel theoretical insight that the optimal temperature $$T^*$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>T</mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> is uniquely determined by the dimensionality of the feature representations, thereby enabling training-free determination of $$T^*$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>T</mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> . Despite this theoretical grounding, empirical evidence reveals that $$T^*$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>T</mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> fluctuates under practical conditions owing to variations in models, datasets, and other confounding factors. To address these influences, we propose and optimize a set of temperature determination coefficients that specify how $$T^*$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>T</mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> should be adjusted based on the theoretical relationship to feature dimensionality. Additionally, we insert a batch normalization layer immediately before the output layer, effectively stabilizing the feature space. Building on these coefficients and a suite of large-scale experiments, we develop an empirical formula to estimate $$T^*$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>T</mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> without additional training while also introducing a corrective scheme to refine $$T^*$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>T</mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> based on the number of classes and task complexity. Our findings confirm that the derived temperature not only aligns with the proposed theoretical perspective, but also generalizes effectively across diverse tasks, consistently enhancing classification performance and offering a practical, training-free solution for determining $$T^*$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>T</mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> . Computational Science and Engineering ; Softmax function ; Feature (linguistics) ; Domain (mathematical analysis) ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Machine learning ; Mathematics ; Artificial neural network ; Mathematical analysis Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Machine Learning and Data Classification
Authors: Kwankamon Dittakan | Jirawat Thaenthong | Sulakkana Rodkuen | Phutphisit Thungklang
Source: Current Applied Science and Technology (article, 2025-10-10)
Field: Chemistry — Analytical Chemistry

Fungus Classification in Peanuts from Smart Lens Imagery Using Convolutional Neural Network Classification of fungi in peanuts remains a critical challenge due to the microscopic nature of fungi, which requires specialized inspection methods. Without proper classification tools, there is a risk for consumers who consume contaminated peanuts, which can lead to severe health effects, particularly for those with fungal allergies. Traditional methods using microscopes or visual inspection by experts are impractical due to the bulky size of instruments, high cost, time-consuming process, and the potential for human error. This research addresses these limitations by proposing an efficient method for fungi classification in peanuts using convolutional neural network (CNN) and image processing techniques. The system utilizes a portable smart lens, an imaging device with high magnification (up to 50x), to capture detailed peanut images and paired with three CNN architectures: MobileNetv2, DenseNet121, and NASNetMobile. The experimental results demonstrated optimal performance with specific parameters for different peanut types. For ground peanuts, the system achieved 97.13% accuracy using 500x500 pixel grayscale images. Similarly, for peanut seeds, the system maintained 96.09% accuracy with 500x500 pixel RGB color images. This approach offers a practical, portable, and cost-effective solution for reliable fungal classification in peanuts. Convolutional neural network ; RGB color model ; Grayscale ; Pixel ; Pattern recognition (psychology) ; Magnification ; Image processing ; Artificial neural network Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Amarjeet
Source: International Journal of Physics and Mathematics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

FIELD-Tunable topological properties of low-dimensional quantum materials The tunability of topological phases in low-dimensional quantum materials under external fields has emerged as a transformative strategy for designing next-generation quantum and nanoelectronic devices. This study systematically investigates the influence of electric fields, magnetic fields, mechanical strain, and multi-field coupling on the topological properties of two-dimensional (2D) quantum systems. Using a combination of ab initio density functional theory (DFT), tight-binding models, and experimental probes such as angle-resolved photoemission spectroscopy (ARPES), scanning tunneling microscopy (STM), and SQUID magnetometry, we demonstrate the controllable transition between trivial and topological phases, modulation of band gaps, and induction of Weyl and Dirac nodes under symmetry-breaking perturbations. Special emphasis is placed on the synergistic effects arising from dual or hybrid external fields. We also highlight how strain and pressure serve as non-destructive tools for phase tuning. Our results not only underscore the fundamental physics behind field-induced topological transitions but also offer a design blueprint for quantum field-effect transistors, spintronic and valleytronic devices, and topologically protected logic units. This tunability opens exciting avenues in reconfigurable quantum technologies where material properties can be dynamically tailored in real time. Field (mathematics) ; Quantum ; Physics ; Topology (electrical circuits) ; Materials science ; Quantum mechanics ; Mathematics ; Engineering ; Pure mathematics ; Electrical engineering Graphene research and applications ; Algebraic structures and combinatorial models ; Catalysis and Oxidation Reactions
Authors: Ashok K. Yadav | Vivek Kumar Jain
Source: Research Square (preprint, 2025-08-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Effect of Cobalt Addition on Magnetism, Half Metallic and Optical Properties of Fe 2 MnAl Heusler Alloy Magnetism ; Alloy ; Cobalt ; Materials science ; Metal ; Metallurgy ; Condensed matter physics ; Physics Heusler alloys: electronic and magnetic properties