jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shunying Ji | Yixuan Sun | Haiyang Zhang
Source: Forests (article, 2025-08-28)
Field: Materials Science — Ceramics and Composites

A Minireview on Multiscale Structural Inheritance and Mechanical Performance Regulation of SiC Wood-Derived Ceramics via Reactive Sintering and Hot-Pressing Wood-derived ceramics represent a novel class of bio-based composite materials that integrate the hierarchical porous architecture of natural wood with high-performance ceramic phases such as silicon carbide (SiC). This review systematically summarizes recent advances in the fabrication of SiC woodceramics via two predominant sintering routes—reactive infiltration sintering and hot-press sintering—and elucidates their effects on the resulting microstructure and mechanical properties. This review leverages the intrinsic anisotropic vascular network and multiscale porosity and mechanical strength, achieving ultralightweight yet mechanically robust ceramics with tunable anisotropy and dynamic energy dissipation capabilities. Critical process–structure–property relationships are highlighted, including the role of ceramic reinforcement phases, interfacial engineering, and multiscale toughening mechanisms. The review further explores emerging applications spanning extreme protection (e.g., ballistic armor and aerospace thermal shields), multifunctional devices (such as electromagnetic shielding and tribological components), and architectural innovations including seismic-resistant composites and energy-efficient building materials. Finally, key challenges such as sintering-induced deformation, interfacial bonding limitations, and scalability are discussed alongside future prospects involving low-temperature sintering, nanoscale interface reinforcement, and additive manufacturing. This mini overview provides essential insights into the design and optimization of wood-derived ceramics, advancing their transition from sustainable biomimetic materials to next-generation high-performance structural components. This review synthesizes data from over 50 recent studies (2011–2025) indexed in Scopus and Web of Science, highlighting three key advancements: (1) bio-templated anisotropy breaking the porosity–strength trade-off, (2) reactive vs. hot-press sintering mechanisms, and (3) multifunctional applications in extreme environments. Sintering ; Ceramic ; Inheritance (genetic algorithm) ; Materials science ; Pressing ; Hot pressing ; Composite material ; Biology ; Gene ; Genetics Advanced ceramic materials synthesis ; Additive Manufacturing and 3D Printing Technologies ; Aluminum Alloys Composites Properties
Authors: Sanni Määttänen | Antti J. Karttunen
Source: Dalton Transactions (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Structure prediction of mixed-anion perovskites: systematic approach integrating octahedral tilting and anion ordering We present a systematic workflow for the structure prediction of mixed-anion perovskites. We consider both octahedral tilting and anion ordering in perovskites to predict potential new oxyfluoride, oxynitride, and nitrofluoride perovskites. The workflow is as follows: (1) pre-screen potential mixed-anion perovskite compositions with the <i>τ</i> tolerance factor, (2) build anion-ordered structure models for 11 perovskite tilt systems, (3) filter the ordered models with a machine-learning methodology, and (4) compare the energetics of the predicted mixed-anion perovskites to experimentally known reference compounds using hybrid density functional methods. We studied 16 novel mixed-anion perovskite compositions, where each composition included approximately 200 000 anion-ordered models. Of these 16 studied mixed-anion perovskite compositions, six are predicted to be energetically favorable compared to experimentally known competing phases. Octahedron ; Ion ; Materials science ; Crystallography ; Chemistry ; Organic chemistry Solid-state spectroscopy and crystallography ; Perovskite Materials and Applications ; Crystal Structures and Properties
Authors: Johannes Gorges | Marianne Engeser | Stefan Grimme
Source: ChemRxiv (preprint, 2025-07-13)
Field: Chemistry — Electrochemistry

Evaluation of the QCxMS2 method for the calculation of collision-induced-dissociation spectra via automated reaction network exploration Collision-induced dissociation mass spectrometry (CID-MS) is an important tool in analytical chemistry for the structural elucidation of unknown compounds. The theoretical prediction of CID spectra plays a critical role in supporting and accelerating this process. To this end, we adapt the recently developed QCxMS2 program originally designed for the calculation of electron ionization (EI) spectra to enable the computation of CID-MS. To account for the fragmentation conditions characteristic of CID within the automated reaction network discovery approach of QCxMS2 we adapted the internal energy distribution to match the experimental conditions. This distribution can be adjusted via a single parameter to approximate various activation settings, thereby eliminating the need for explicit simulations of the collisional process. We evaluate our approach on a test set of 13 organic molecules with diverse functional groups, compiled specifically for this study. All reference spectra were recorded consistently under the same measurement conditions, including both CID and higher-energy collisional dissociation (HCD) modes. Overall, QCxMS2 achieves good average entropy similarity scores (ESS) of 0.687 for the HCD spectra and 0.773 for the CID spectra. The direct comparison to experimental data demonstrates that the QCxMS2 approach, even without explicit modeling of collisions, is generally capable of computing both CID and HCD spectra with reasonable accuracy and robustness. This highlights its potential as a valuable tool for integration into structure elucidation workflows in analytical mass spectrometry. Collision ; Dissociation (chemistry) ; Collision-induced dissociation ; Computer science ; Spectral line ; Chemistry ; Computational chemistry ; Physics ; Physical chemistry ; Mass spectrometry ; Programming language ; Chromatography ; Quantum mechanics Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses
Authors: Chetan R Chilkunda | John R. Kitchin | Robert D. Tilton
Source: Digital Discovery (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

A classification-based methodology for the estimation of binary surfactant critical micelle concentrations High throughput experiments with classification and regular solution theory is used to identify the critical micelle concentration in mixed surfactants. Pulmonary surfactant ; Critical micelle concentration ; Binary number ; Micelle ; Estimation ; Computer science ; Chromatography ; Materials science ; Chemistry ; Mathematics ; Engineering ; Organic chemistry ; Biochemistry ; Aqueous solution ; Systems engineering Surfactants and Colloidal Systems ; Fluid Dynamics and Mixing ; Plant Surface Properties and Treatments
Authors: Y. Zhang | Hui Lin Jiang | Jun Ling | Liliang Wen | Keliang Yan | Aiming Chen | Zhongda Zeng | Miaomiao Wang | Qianxu Yang
Source: International Journal of Analytical Chemistry (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Comprehensive Comparison of Similarity Evaluation and Discovery of Weak Spectral Variations of Near‐Infrared Spectroscopy for Tobacco Formulation Replacement Near-infrared (NIR) spectroscopy data encounter challenges in data processing such as peak overlapping, information redundancy, and background or noise, which complicate the evaluation of weak differences among similar samples. Therefore, accurately identifying these differences and assessing similarities are essential in practical applications for sample classification and further replacement of raw materials in the product formulation. In this work, 32 data preprocessing strategies of NIR data were systematically combined for comprehensive comparison, and 11 methods for similarity analysis were evaluated to attain optimal performance. Using the rationality of similarity evaluation as the assessment criterion, the combination of NIR data pretreatment methods of "standard normal variate (SNV) + first-order derivative by Savitzky-Golay (1D/SG) + maximum-minimum scaling (MMS) + spectral similarity by combinatorial strategy (SS/CS)" is ultimately preferred as the most effective combination for similarity evaluation. It uses SNV transformation, 1D/SG, MMS, and scattering correction to eliminate the scattering effect, enhance the signal-to-noise ratio (SNR) of the distinction of overlapping peaks, and improve data comparability. After this, the widely used methods for similarity evaluation were employed for comprehensive analysis and comparison of the rationality, such as Euclidean distance, correlation coefficient, and divergence information. The evaluation strategy proposed in this work can effectively distinguish the difference among the tobacco samples existing in 10 different categories. The similarity among typical samples in the same class is above 0.9, while the values in different classes are below 0.7. In real applications for method validation, recognition precision of tobacco samples with blending of interfering mixtures reaches 5%, which is conducted using complex tobacco materials for formulation replacement and optimization. The satisfactory results introduce robust and CS that outperforms traditional single-method approaches to resolve weak spectral differences through real-world tobacco formulation replacement applications. It can be widely used in the areas related to NIR for similarity evaluation, such as pharmaceuticals, food quality control, and environmental monitoring. Similarity (geometry) ; Preprocessor ; Pattern recognition (psychology) ; Euclidean distance ; Multidimensional scaling ; Correlation ; Data pre-processing ; Sample (material) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Lin Liu | Qixing Tang | Juan Liao | Lu Liu | Yujun Zhang | Li Guang Jiao
Source: INMATEH Agricultural Engineering (article, 2025-04-09)
Field: Chemistry — Analytical Chemistry

NON-DESTRUCTIVE DETECTION OF MOLD IN MAIZE USING NEAR-INFRARED SPECTRAL FINGERPRINTING Mold contamination of stored maize can cause significant economic losses, and it is crucial to effectively classify maize kernels without destroying their original structure. But existing studies have found it difficult to distinguish moldy maize. In this paper, a method for non-destructive detection of mold in maize using near-infrared spectral fingerprinting is proposed. The spectral raw data are initially acquired using a handheld near-infrared spectrometer. To enhance the signal quality, preprocessing is conducted, and a classification model is developed for full-band spectral data. In order to further optimize the model and enhance the classification accuracy, the feature wavelengths were extracted from the spectral data with effective preprocessing techniques in the full-band model. Finally, the maize kernel mold classification model is constructed. The classification accuracy of SG+SNV-SVM-ISFLA model can reach up to 97.22%, and the accuracy for the identification of asymptomatic moldy maize is 96.30%, which can realize the accurate grading of moldy accurate classification of maize and can well distinguish asymptomatic moldy maize. This work may significantly control the spread of molds in the food industry while improving storage economics and safety. Mold ; Environmental science ; DNA profiling ; Materials science ; Composite material ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Meat and Animal Product Quality
Authors: Mariana Fazio | Manel Martínez‐Ramón | Salvador Sosa Güitrón | M. Babzien | Mikhail Fedurin | Junjie Li | Mark Palmer | Sandra Biedron
Source: ArXiv.org (preprint, 2025-05-19)
Field: Materials Science — Surfaces, Coatings and Films

Unsupervised anomaly detection in MeV ultrafast electron diffraction MeV ultrafast electron diffraction (MUED) is a pump-probe technique used to study the dynamic structural evolution of materials. An ultrashort laser pulse triggers structural changes, which are then probed by an ultrashort relativistic electron beam. To overcome low signal-to-noise ratios, diffraction patterns are averaged over thousands of shots. However, shot-to-shot instabilities in the electron beam can distort individual patterns, introducing uncertainty. Improving MUED accuracy requires detecting and removing these anomalous patterns from large datasets. In this work, we developed a fully unsupervised methodology for the detection of anomalous diffraction patterns. Using a convolutional autoencoder, we calculate the reconstruction mean squared error of the diffraction patterns. Based on the statistical analysis of this error, we provide the user an estimation of the probability that the pattern is normal, which also allows a posterior visual inspection of the images that are difficult to classify. This method has been trained with only 100 diffraction patterns and tested on 1521 patterns, resulting in a false positive rate between 0.2\% and 0.4\%, with a training time of 10 seconds per image and a test time of about 1 second per image. The proposed methodology can also be applied to other diffraction techniques in which large datasets are collected that include faulty images due to instrumental instabilities. Anomaly detection ; Detector ; Measure (data warehouse) ; Focus (optics) ; Anomaly (physics) ; Ultrashort pulse Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Nuclear Physics and Applications
Authors: Ana Beatriz Silva Araújo | Mário Sérgio Lorenço | Danilo José Machado de Abreu | Laryanne Lopes de Carvalho Dias | Elisângela Elena Nunes Carvalho | Eduardo Valério de Barros Vilas Boas
Source: DELOS Desarrollo Local Sostenible (article, 2025-04-30)
Field: Chemistry — Analytical Chemistry

Artificial neural network and statistical tools applied to determine the nutritional composition of Barbarea verna between three different growth states A comparative study was conducted with upland cress (Barbarea verna) at three developmental states after sowing: microgreen (23 days), baby leaf (30 days) and commercial (48 days). Unpelleted seeds of upland cress were sown on phenolic foam substrate. Sixteen days after sowing, the seedlings were transferred to a hydroponic system and then three harvests were made. The analyses performed were mineral profile, phenolic compounds, antioxidant capacity, coloration, pH, titratable acidity, total chlorophyll and total carotenoids. Microgreen state had 12 times more iron and 2 times more magnesium, while at the baby leaf state it had more calcium and 2 times more iron, compared to commercial. These results suggest that consumption of young leaves of upland cress contributes to a higher intake of minerals and bioactive compounds. In addition, the use of artificial neural network has proven to be a useful statistical tool for performing data analysis for clustering by similarity. Artificial neural network ; Composition (language) ; Artificial intelligence ; Computer science ; Biological system ; Biology Spectroscopy and Chemometric Analyses
Authors: Patipat Thanompongchart | Pakamon Pintana | Tossapon Katongtung | James Moran | Nakorn Tippayawong
Source: INMATEH Agricultural Engineering (article, 2025-08-25)
Field: Chemistry — Analytical Chemistry

PREDICTION OF BOESENBERGIA ROTUNDA (L.) DRYING USING HOT AIR AND ULTRASONIC VIBRATION VIA EXTREME GRADIENT BOOSTING This study used eXtreme Gradient Boosting (XGBoost) to predict the drying behavior of Boesenbergia rotunda (L.) under hot air and ultrasonic vibration. A dataset of 73 samples with temperature, time, and vibration as inputs was used. The model achieved high accuracy with R² = 0.99 and RMSE = 8.71 on test data. SHAP analysis revealed that ultrasonic vibration improved moisture reduction, especially during longer drying times. These results demonstrate the effectiveness of combining XGBoost and SHAP for understanding and optimizing complex drying processes in food and agricultural engineering. Ultrasonic sensor ; Vibration ; Boosting (machine learning) ; Gradient boosting ; Acoustics ; Environmental science ; Materials science ; Structural engineering ; Engineering ; Physics ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Food Drying and Modeling ; Smart Agriculture and AI
Authors: Federico Lazzari | Luigi Crisci | Vincenzo Barone
Source: Small Structures (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

From Molecular Bricks to Sustainable Nanomaterials: An Accurate and Scalable Unsupervised Workflow The rational design of sustainable nanomaterials increasingly depends on organic molecular units whose optical, electronic, and catalytic properties stem from minute structural details. Achieving the required level of accuracy—beyond standard density functional theory (DFT)—is crucial to disentangle intrinsic geometrical features from environment‐induced effects in applications such as organocatalysis, organic light‐emitting diodes, and dye‐sensitized solar cells. Here we introduce an adaptive accuracy framework rooted in the Pisa Composite Schemes (PCS), combining rigorous quantum‐mechanical foundations with scalable efficiency. The reference PCS2 protocol, based on explicitly correlated coupled‐cluster (CC‐F12) theory, achieves spectroscopic accuracy for medium‐sized molecules, while the BDPCS3 model extends the reach of PCS methods to much larger systems through double‐hybrid DFT and bond corrections. Together, they form a hierarchical and internally consistent strategy that balances precision and cost across molecular scales. Applications to quinone derivatives, aromatic biomolecules, amino acids, and light‐driven molecular machines demonstrate predictive accuracy and transferability. Seamless integration with the Nano‐LEGO platform further enables automated fragment recognition and reconstruction, paving the way for the data‐driven design of functional molecular materials. Overall, this framework bridges accuracy and scalability, offering a practical route toward predictive modeling and rational design in next‐generation nanoscience. Scalability ; Workflow ; Rational design ; Functional design ; Fragment (logic) Luminescence and Fluorescent Materials ; Supramolecular Chemistry and Complexes ; Synthesis and Properties of Aromatic Compounds
Authors: Sorayya Kazemi | Arash Dalili | Abbas S. Milani
Source: Polymer Composites (article, 2025-09-17)
Field: Materials Science — Polymers and Plastics

Global Symmetry, Sphericity, and Roundness in Hemisphere Forming of Fabric Reinforcements: Associations With Local Fiber Distortions and a Black‐Box Quality Prediction ABSTRACT An experimental analysis is conducted to identify the effect of design variables on a set of global geometrical quality metrics, including sphericity, roundness, and symmetry, in hemisphere draping of woven fabrics, next to the commonly employed primary (local) quality metrics concerning the fiber distortions. These secondary global metrics may be deemed important in practice, for example, during post‐forming processes and assembly of draped parts. Thirty punch‐forming configurations were designed and tested by varying process control factors, including (1) the fabric orientation, (2) the number of layers, (3) the number of clamps on the blank holder, and (4) the pressure distribution on the clamps. It was found that most samples with a fully uniform distribution of clamping pressure and sufficiently high mean pressure, result in a roundness value of 90% and above. Next to these control factors, a higher number of clamps (with equally‐spaced positions) generated a higher symmetry in the draped parts—identified via the center of mass displacement of samples. The sphericity for all tested configurations was similar ( ±4%; out of the maximum of 84% for a perfect hemisphere). The study also showed how a basic primary “quality score” as well as “forming quality classes” may be defined and predicted for design. Among the secondary metrics studied, the symmetry was most highly correlated with the primary fiber distortion factors, followed by the roundness. Sphericity ; Roundness (object) ; Clamping ; Blank ; Quality (philosophy) ; Distortion (music) ; Fiber Textile materials and evaluations ; Mechanical Behavior of Composites ; Optical measurement and interference techniques
Authors: Tereza Zemánková | Martin Kizovský | Zdeněk Pilát | Pavlína Modlitbová | Jan Ježek | Martin Šiler | Ota Samek
Source: Methods and Protocols (article, 2025-12-21)
Field: Chemistry — Analytical Chemistry

Raman Spectroscopy for Testing Wood Pellets The creation of bioenergy based on the biomass wood pellet industry, which accounts for the majority of the global biomass supply, is one of the most common and important ways to utilize waste wood, wood dust, and other byproducts of wood manufacturing, known as forestry residues. Pellet production processes might greatly benefit from fast monitoring systems that may allow for at least a semi-quantitative measurement of crucial parameters such as lignin and cellulose. The determination of lignin and cellulose is complicated and time-consuming because it usually requires time-demanding and labor-intensive sample preparation. This, however, might be a crucial problem. In this context, the application of Raman spectroscopic techniques is considered a promising approach, as it enables rapid, reliable, and label-free analysis of wood pellets, providing information about the chemical composition of the biomass, specifically lignin and cellulose. The purpose of this article is to report on the application of Raman spectroscopy exemplified by the detection of the lignin/cellulose ratio. In our methodological approach, we integrated the area under the selected Raman bands to avoid a large scatter of data when only the intensities of the bands were used. Moreover, the acquired Raman spectra displayed very strong signals from both substances, which contributes to the feasibility of the analysis even with a portable instrument. This study is expected to be of assistance in situations when the monitoring of the chemical changes and the quick inspection of pellets are required in near real time, online, and in situ. Lignin ; Raman spectroscopy ; Pellets ; Pellet ; Cellulose ; Biomass (ecology) ; Bioenergy ; Renewable energy Spectroscopy and Chemometric Analyses ; Advanced Cellulose Research Studies ; Spectroscopy Techniques in Biomedical and Chemical Research