jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiao, Yan | Yao ,   Dongming
Source: Journal of Combinatorial Mathematics and Combinatorial Computing (article, 2025-03-30)
Field: Chemistry — Analytical Chemistry

Deep Learning Based Peach Quality and Disease Detection Methods for Research in Intelligent Food Safety Detection With the rapid development of China's economic level and the significant improvement of people's living standard, the quality issue of peaches has become more and more strict.In this paper, based on deep learning algorithm, we propose the recognition method of peach fruit color, size and fruit shape features, combined with near-infrared spectroscopy detection technology, to quantify the peach fruit components and discriminate its maturity.Differential algorithm, standard normal transform, and multiple scattering correction are applied to pre-process peach fruit data.Based on M-YOLOv5s target detection framework, spectral analysis and image characterization techniques were used to jointly detect the degree of peach fruit disease.The distribution of peach fruit quality parameters was investigated, and the test results showed that 39.19% of the samples with measured values of fruit size were concentrated at 1.60-6.40cm, and 61.79% of the samples with predicted values were concentrated at 2.50-7.50cm, which was located at around the mean value of 4.763 cm.The classification accuracies of the information modeling set and validation set for the combination of the spectral analysis and image eigenvalue detection techniques were 91.439% and 88.487%, respectively, and the combined use of the two techniques had a high accuracy for the differentiation of diseased peach fruits.Based on the experimental results, the application of spectral detection technology in food freshness detection as well as pesticide residues and illegal additives is explored. Quality (philosophy) ; Computer science ; Food safety ; Deep learning ; Artificial intelligence ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Emma L. Matthewman | Jonathan Sperry | Cameron C. Weber
Source: ChemistryOpen (article, 2025-02-25)
Field: Chemistry — Organic Chemistry

Efficient Production and Isolation of 3‐Acetamido‐5‐Acetylfuran from N‐Acetyl‐D‐Glucosamine within Protic Ionic Liquids The transformation of chitin and its monomer N-acetylglucosamine (NAG) to high-value organonitrogen chemicals has attracted increasing interest, with 3-acetamido-5-acetylfuran (3A5AF) proposed as a versatile platform chemical. The preparation of 3A5AF from NAG has relied on high boiling organic solvents, ionic liquids (ILs) or deep eutectic solvents (DES). While these methods have met with some success, the isolation of 3A5AF and recycling of the solvent remains problematic for non-IL methods whereas most IL methods utilize inherently expensive aprotic ILs with substantial environmental footprints. This study details the preparation of 3A5AF in more cost-effective chloride-based protic ILs (PILs) with lower synthetic footprints than conventional ILs. Maximum yields of 42.5 %, 51.5 % and 57.0 % of 3A5AF were afforded in 1,8-diazabicyclo[5.4.0]undec-7-ene chloride ([DBU]Cl), tripropylammonium chloride ([TPA]Cl) and tributylammonium chloride ([TBA]Cl) respectively with 2 eq. B(OH)<sub>3</sub> at 150 °C. The 3A5AF formed was readily isolated by simple solvent extraction, avoiding column chromatography, with selected systems displaying good recyclability and scalability. E-factor calculations revealed that the PIL methodology produced substantially less waste than approaches for the production of 3A5AF from molecular solvents and DES, highlighting that PILs are suitable solvents for the sustainable production of 3A5AF. Ionic liquid ; Chemistry ; Ionic bonding ; Isolation (microbiology) ; Glucosamine ; Combinatorial chemistry ; Organic chemistry ; Ion ; Bioinformatics ; Biology ; Catalysis Chemical Synthesis and Reactions ; Ionic liquids properties and applications ; Catalysis for Biomass Conversion
Authors: Beatrice Caviglia | Stepan Timr | Marianne Guiral | Marie-Thérèse Giudici-Orticoni | Tilo Seydel | Christian Beck | Judith Peters | Fabio Sterpone | Alessandro Paciaroni
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-06-27)
Field: Chemistry — Spectroscopy

Cytoplasmic Fluidity and the Cold Life: Proteome Stability is Decoupled from Viability in Psychrophiles Protein diffusion, critical for cellular metabolism, occurs in the highly crowded cytoplasm. Understanding how this dynamics changes when organisms are adapted to different thermal niches is a fundamental challenge in microbiology and biophysics. In Escherichia coli , protein diffusion undergoes a pronounced slowdown at temperatures near cellular death, coinciding with the early stages of unfolding. To determine whether this phenomenon is universal, we investigated psychrophilic and hyperthermophilic bacteria. In both species, a marked diffusion slowdown takes place at the onset of proteome melting. However, while the dynamic arrest is associated with the thermal death point for the hyperthermophilic proteome, the psychrophilic proteome maintains substantial mobility well beyond the cellular inactivation. The decoupling between metabolic viability and proteome dynamics and stability suggests that the functional processes of psychrophilic bacteria are extremely temperature sensitive. This finding echoes the behaviour of psychrophilic enzymes, manifesting a large temperature gap between optimal activity and unfolding. Protein diffusion is optimised to maintain functional fluidity at the organism’s working conditions, but its temperature dependence is controlled by the proteome folded state. Our findings redefine the relationship between cytoplasmic dynamics, proteome stability, and bacterial survival in cold environments. Psychrophile ; Cytoplasm ; Proteome ; Biology ; Chemistry ; Cell biology ; Biochemistry ; Enzyme Advanced Proteomics Techniques and Applications
Authors: Sofia Aleksandrovna Shevtsova | Мariia S. Saveleva | Oksana A. Mayorova | Ekaterina S. Prikhozhdenko
Source: Izvestiya of Saratov University Physics (article, 2025-08-21)
Field: Chemistry — Analytical Chemistry

Effect of low concentrations of hyaluronic acid on the structure of whey protein isolate during conjugation: Development and optimization of machine learning models based on adaptive boosting for spectroscopic data analysis . () (, 0.1-0.5%) () . , 1003 -1 , . , (AdaBoost). ( max_depth=3, 325) (98.3%) (R = 0.91) 300 . (GridSearchCV). . (763, 1003, 1240, 1400 -1 ), . - . Hyaluronic acid ; Boosting (machine learning) ; Chemistry ; Whey protein isolate ; Whey protein ; Machine learning ; Artificial intelligence ; Computer science ; Chromatography ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Food Industry and Aquatic Biology
Authors: Kui-Fern Chin | Chang-Yii Chai | Ismail Saad | Yee-Ann Lee
Source: International Journal of Advanced Computer Science and Applications (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Adaptive Denoising of Partial Discharge Using Absolute Difference Optimization Versus Artificial Neural Networks Accurate partial discharge (PD) localization in medium-voltage (MV) power cables is essential for condition-based maintenance, yet it remains unreliable when PD pulses are masked by broadband noise and narrowband interference. The novelty of this work is a controlled denoiser-to-localization benchmarking framework that isolates the denoising front end, while keeping the downstream PD detection and localization backend fixed, allowing localization differences to be attributed solely to denoising decisions. Within this fixed-backend paradigm, an optimization-driven Adaptive Denoising Optimization (ADO) method is introduced as an adaptive discrete wavelet transform (DWT) front end that systematically selects the mother wavelet, decomposition level, and threshold parameters to preserve time-of-arrival (ToA) critical wavefront features rather than only maximizing noise suppression. ADO is evaluated against two learning-based denoisers, a multilayer artificial neural network (ANN) and a lightweight feedforward neural network (FNN), using MATLAB simulations of synthetic PD pulses corrupted by white Gaussian noise (WGN) and discrete spectral interference (DSI) over SNRs from 9.78 dB to -10.34 dB. Performance is quantified using execution time, percentage localization error (PE), median absolute localization error (MedAE), and F1 score. Results show that ADO delivers the most robust localization fidelity, maintaining near-zero PE above -6 dB, keeping PE below 0.3% at -10.34 dB, achieving sub-metre MedAE, and sustaining F1 close to 1.0 across noise levels. In contrast, FNN is the fastest option, reducing runtime by approximately 15% versus ANN and 27% versus ADO, highlighting a practical robustness-efficiency trade-off for real-time MV cable monitoring. Noise reduction ; Noise (video) ; Artificial neural network ; Gaussian noise ; White noise ; Additive white Gaussian noise ; Front and back ends ; Backpropagation ; Pattern recognition (psychology) High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Thermal Analysis in Power Transmission
Authors: Wei Cai | Yiming Yu | Wei Fu | Tao Deng | Chenyu Ma | Yifan Wang | Xi Zhang | Chengqiang Cui | Yao Xu | Tingyi Zhang | Shangqi Diao | Di Li | Songqing Lin | Yuan Gao | Yi Li
Source: Research Square (preprint, 2025-05-14)
Field: Chemistry — Electrochemistry

On-Chip Trace Detection of Heavy Metal Ions in Extreme-Deep Seawater Using CMOS-Integrated Low-Noise Transimpedance Amplifiers Transimpedance amplifier ; Seawater ; CMOS ; Chip ; Amplifier ; TRACE (psycholinguistics) ; Noise (video) ; Ion ; Materials science ; Optoelectronics ; Physics ; Operational amplifier ; Electrical engineering ; Computer science ; Engineering ; Oceanography ; Geology ; Artificial intelligence Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Advanced Memory and Neural Computing
Authors: Akihiko Ikeda | Yuya Kubota | Yuto Ishii | Xu-Guang Zhou | Shiyue Peng | Hiroaki Hayashi | Yasuhiro H. Matsuda | Kosuke Noda | Tomoya Tanaka | Kotomi Shimbori | Kenta Seki | Hideaki Kobayashi | Dilip Bhoi | Masaki Gen | K. Gautam | Mitsuru Akaki | Shiro Kawachi | Shusuke Kasamatsu | Toshihiro Nomura | Yuichi Inubushi | Makina Yabashi
Source: ArXiv.org (preprint, 2025-04-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

X-ray free-electron laser observation of giant and anisotropic magnetostriction in $β$-O$_{2}$ at 110 Tesla In strong magnetic fields beyond 100 T, the significant Zeeman energy competes with the lattice interactions, where a considerable magnetostriction is expected. However, the microscopic observation of the magnetostriction above 100 T has been hindered due to the short pulse duration of $μ$-seconds and the coil's destruction. Here, we report the observation of the giant and anisotropic magnetostriction of $\sim 1$ % at 110 T in the spin-controlled crystal, $β$-O$_{2}$, by combining the single-shot diffraction of x-ray free-electron laser (XFEL) and the newly developed portable 100 T generator (PINK-02). The very soft and anisotropic response of $β$-O$_{2}$ should originate in the competing van der Waals force and exchange interaction, and also the frustration of spin and lattice on the triangular network. The XFEL experiment above 100 T using PINK-02 enables microscopic investigations on materials' properties at high magnetic fields, providing insights into how spins contribute to the stability of crystal structures. Magnetostriction ; Anisotropy ; Zeeman effect ; Magnetic anisotropy ; Spins ; Laser ; van der Waals force ; Anisotropy energy Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides ; Magnetic properties of thin films
Authors: Jakhfer Alikhanov | Tsvetelina Georgieva | Eleonora Nedelcheva | Айдар Молдажанов | Akmaral Kulmakhambetova | D. A. Zinchenko | Alisher Nurtuleuov | Zhandos Shynybay | Plamen Daskalov
Source: Preprints.org (preprint, 2025-07-29)
Field: Chemistry — Analytical Chemistry

Digital Identification of Different Varieties of Kazakhstan Apples Using Deep Learning Techniques Application of pre-trained CNN and transfer learning approach for identification of different varieties of Kazakhstan apples is presented in the paper. The study used the most popular Kazakhstan apple varieties as: Aport Alexander, Ainur, Sinap Almaty, Nursat and Kazakhskij Yubilejnyj. In this study, two pre-trained CNN networks, SqueezeNet and GoogLeNet, were fine-tuned using different values of the Initial Learning Rate (ILR) parameter of 0.0001, 0.0002, 0.00025, 0.0003, 0.00035, 0.0004, 0.0005 and three network tuning algorithms, respectively The Stochastic Gradient Descent with moment solver (Sgdm), the Adam optimization algorithm (Adam), and Root Mean Square Propagation (RMSprop). The performance of the networks was evaluated by analyzing the values of Training Accuracy (TA), Training Loss (TL), Validation Accuracy (VA), Validation Loss (VL), and Confusion Matrix. The training of the networks was performed in 1900 iterations and 30 epochs. The developed approach for the identification of five varieties of Kazakhstan apples using deep learning techniques achieved 100% correct classification of fruits for the Ainur variety with a GoogLeNet network, solver RMSprop and ILR= 0.0005. For the varieties Aport, Kazakhski Yubileinyi and Nursat, one of the three network evaluation indicators achieves 100% accuracy, and for Sinap Almatynski – all three indicators are with and above the value of 95%. For varieties Ainur, Aport, Kazakhski Yubileinyi and Sinap Almatynski as the best (with the highest optimal accuracy values) is the GoogLeNet network, with the following settings: solver RMSprop and ILR=0.0005. Only for variety Nursat is the SqeezeNet network suitable, with the following settings: solver Sgdm and ILR= 0.0003. The proposed approach for recognizing the varietal affiliation of apples using deep learning neural networks is suitable for the analyzed apple varieties and could be easily implemented and used in industrial conditions for sorting fruits. The achieved recognition accuracy meets the requirements in the field. Identification (biology) ; Artificial intelligence ; Computer science ; Horticulture ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Kevin Zhu | Daniel Flam-Shepherd | Alán Aspuru-Guzik
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Chemical language models can generate biomolecules atom-by-atom Chemical language models learn atom-level representations and generate biomolecules, capturing sequence-to-structure features. This work shows their potential to design protein-drug conjugates by simultaneously exploring chemical and protein space. Biomolecule ; Atom (system on chip) ; Chemistry ; Computer science ; Nanotechnology ; Materials science ; Parallel computing Machine Learning in Materials Science ; Machine Learning in Bioinformatics
Authors: Subhavna Juneja | Sudipta Biswas | Kang Rong | Alan X. Wang
Source: Sensors & Diagnostics (article, 2025-11-29)
Field: Materials Science — Biomaterials

Reproducible single-molecule optofluidic-SERS analysis on nanostar-activated diatom biosilica capsules Single-molecule (SM) analysis with surface-enhanced Raman scattering (SERS) based on plasmonic nanostars on diatom photonic crystal biosilica. Diatom ; Raman scattering ; Raman spectroscopy ; Light scattering ; Analytical Chemistry (journal) Diatoms and Algae Research ; Marine and coastal ecosystems ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Aishwaryo Ghosh | Amitava Moitra | Tanusri Saha‐Dasgupta
Source: Journal of Physics Materials (article, 2025-01-17)
Field: Materials Science — Materials Chemistry

Ab-initio trained machine learning potential for MAX compound Ti<sub>2</sub>AlC: construction, validation, and study of non linear elasticity Abstract One of the intriguing features exhibited by the layered MAX phase compounds, is the nonlinear elastic behaviour. Since the experimental observation of this curious behaviour, the underlying micro-mechanism has been discussed to interpret experimental observations. However, the theoretical investigation remained a challenge due to the associated length and time scales of the phenomena. In the present work, we adopt a data driven approach to develop a machine learned interatomic potential for the MAX compound Ti 2 AlC following the moment tensor potential protocol. The constructed potential is validated in lattice constant, formation energy, elastic constant, and stacking fault energies. Finally, applying machine learned potential in classical molecular dynamics provides a faithful representation of the experimentally observed nonlinear elasticity for Ti 2 AlC. The generated atomic configurations confirm the proposal of formation of ripplocations which allow atomic layers to glide relative to each other without breaking the in-plane bonds. We find common defects, like Al vacancy, strongly influence the hysteresis properties of the stress–strain curve, paving the route to defect-engineered nonlinear elasticity. Ab initio ; Elasticity (physics) ; Artificial intelligence ; Machine learning ; Computer science ; Materials science ; Physics ; Composite material ; Quantum mechanics MXene and MAX Phase Materials ; Aluminum Alloys Composites Properties ; Boron and Carbon Nanomaterials Research
Authors: J Kushal | Chithra Loganathan | Rajesh Reesu | Sheetij Dutta | Arghya Paik | Anjan Kr. Dasgupta | G. Niranjan Reddy | Athishay Suchindar
Source: ChemistrySelect (article, 2025-10-31)
Field: Chemistry — Organic Chemistry

Recent Advances in Quinoline Derivatives: Biological and Medicinal Insights Abstract Quinoline derivatives are a heterocyclic family of structurally diverse and pharmacologically relevant compounds of interest in medicinal chemistry. The molecules exhibit a wide range of biological functions, such as antimicrobial, anti‐inflammatory, anticancer, antimalarial, antioxidant and antibacterial agents, which enable them to be considered useful as drug discovery and development scaffolds. This review aims to give a general overview of pharmacological potential of quinoline derivatives including their action mechanisms, clinical and therapeutic use. Further, the recent reports on solubility optimization, approach to drug delivery, and formulation are addressed with the strong interest in their role in enhancing pharmacokinetic characteristics and clinical translation. Special emphasis is placed on the up‐to‐date studies on synthetic strategies and hybrid molecules that support the flexibility of the quinoline skeleton in terms of its structure. Through a combination of mechanistic understanding and therapy, this review provides an overview of the advancements made in quinolines research and prospects of developing next generation analogs of quinolines with superior efficacy and safety. Quinoline ; Drug discovery ; Drug ; Flexibility (engineering) ; Drug development Synthesis and biological activity ; Multicomponent Synthesis of Heterocycles ; Cancer therapeutics and mechanisms
Authors: Jordy Sarmas-Farfan | Antonio Díaz‐Barrios | Teresa Lehmann | Vladimir Alvarado
Source: Preprints.org (preprint, 2025-05-12)
Field: Chemistry — Analytical Chemistry

Demulsification Kinetics of Water-in-Oil Emulsions of Ecuadorian Crude Oil: Influence of Temperature and Salinity This work focuses on the stability analysis of water-in-oil macroemulsions with a crude oil from Sacha Field in Ecuador. This field is an important hydrocarbon resource in Ecuador with typical bottom water drive. The comprehensive stability analysis includes coalescence, water resolution or phase separation, water-oil interfacial tension and interfacial dilatational viscoelastic modulus measurements over time. Two main parameters were controlled in these experiments: water salinity and temperature. This analysis, reported here for the first time for this important resource in Ecuador, sheds light on which mechanisms are likely to control the stability of these macroemulsions. Our results indicate that regardless of temperature, lower salinity favors emulsion stability, likely due to the tendency of a stiffer interface to form at low ionic strength, as interfacial viscoelasticity results show. On the other hand, higher temperatures favor water resolution, possibly due to higher sedimentation rates, which is further favored at higher salinity. Finally, analysis of the interface buildup rates could explain the observed increased in emulsion stability over time. Salinity ; Kinetics ; Crude oil ; Formation water ; Chemistry ; Environmental science ; Petroleum engineering ; Pulp and paper industry ; Environmental chemistry ; Geology ; Oceanography ; Engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: K FAN
Source: Applied and Computational Engineering (article, 2025-04-07)
Field: Chemistry — Spectroscopy

Research on Progress and Development of Superhydrophobic Materials Superhydrophobic materials can realize the effective slippage of liquid on the surface, a with excellent properties such as self-cleaning and anti-pollution, they can be widely used in liquid transportation, anti-pollution coatings, microfluidic transport and other fields. In nature, organisms have evolved many special structures that adapt to their living environment, such as lotus leaves, water striders and other organisms with unique micro and nano structures, showing amazing water-repellent and self-cleaning properties. By studying and mimicking the microstructure of these biological surfaces, scientists have successfully prepared surfaces with excellent superhydrophobic microstructures. The paper will discuss the mechanism of superhydrophobic phenomena and introduce the applications of superhydrophobic materials in anti-icing, anti-oil, anti-fogging and self-cleaning. Finally, by reading the relevant literature, this paper discusses the future development of superhydrophobic materials, the preparation process of superhydrophobic surfaces is still relatively complex, and how to maintain the stability and hydrophobicity of the surface while simplifying the hydrophobic preparation steps is a key challenge for researchers. Nanotechnology ; Materials science Aerogels and thermal insulation ; Surface Modification and Superhydrophobicity