jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guoqiang Zhao | Yi Peng | Kenji Kojima | Yipeng Cai | Xiang Li | Kan Zhao | Shengli Guo | Wei Han | Yongqing Li | Fanlong Ning | Xiancheng Wang | Bo Gu | Gang Su | Sadamichi Maekawa | Y. J. Uemura | Changqing Jin
Source: Nanomaterials (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

Doping Effects on Magnetic and Electronic Transport Properties in (Ba1−xRbx)(Zn1−yMny)2As2 (0.1 ≤ x, y ≤ 0.25) Diluted magnetic semiconductors (DMSs) represent a significant area of interest for research and applications in spintronics. Recently, DMSs derived from BaZn<sub>2</sub>As<sub>2</sub> have garnered significant interest due to the record Curie temperature (<i>T</i><sub>C</sub>) of 260 K. However, the influence of doping on their magnetic evolution and transport characteristics has not been thoroughly investigated. This study aims to fill this gap through susceptibility and magnetization measurements, electric transport analysis, and muon spin relaxation and rotation (µSR) measurements on (Ba<sub>1-x</sub>Rb<sub>x</sub>)(Zn<sub>1-y</sub>Mn<sub>y</sub>)<sub>2</sub>As<sub>2</sub> (0.1 ≤ x, y ≤ 0.25, BRZMA). Key findings include the following: (1) BRZMA showed a maximum <i>T</i><sub>C</sub> of 138 K, much lower than (Ba,K)(Zn,Mn)<sub>2</sub>As, because of a reduced carrier concentration. (2) A substantial electromagnetic coupling is evidenced by a negative magnetoresistance of up to 34% observed in optimally doped BRZMA. (3) A 100% static magnetic ordered volume fraction is achieved in the low-temperature region, indicating a homogeneous magnet. (4) Furthermore, a systematic and innovative methodology has been initially proposed, characterized by clear step-by-step instructions aimed at enhancing <i>T</i><sub>C</sub>, grounded in robust experimental findings. The findings presented provide valuable insights into the spin-charge interplay concerning magnetic and electronic transport properties. Furthermore, they offer clear direction for the investigation of higher <i>T</i><sub>C</sub> DMSs. Spintronics ; Condensed matter physics ; Materials science ; Doping ; Magnetoresistance ; Magnetic semiconductor ; Magnetization ; Curie temperature ; Relaxation (psychology) ; Ferromagnetism ; Physics ; Magnetic field ZnO doping and properties ; Magnetic and transport properties of perovskites and related materials ; Iron-based superconductors research
Authors: Daniela Moyankova | Petya Stoykova | Petya Veleva | N. Christov | Antoniya Petrova | Krasimir Rusanov | Stefka Atanassova
Source: Sensors (article, 2025-12-15)
Field: Chemistry — Analytical Chemistry

Low-Temperature Stress-Induced Changes in Cucumber Plants—A Near-Infrared Spectroscopy and Aquaphotomics Approach for Investigation Low temperatures have a significant impact on the growth, development, and productivity of cucumber plants. The potential of near-infrared spectroscopy and the aquaphotomics approach for investigating chilling stress was studied in Voreas F1 and Gergana cultivars. Changes in the spectral patterns of cucumber plants were compared with physiological and metabolic data. Voreas plants were unable to survive seven days of low-temperature stress due to a drastic increase in electrolyte leakage and a decrease in the net photosynthesis rate, stomatal conductance, and transpiration rate. Gergana plants survived chilling by preserving cell membrane integrity and photosynthesis efficiency. During chilling treatment, the content of most metabolites in both cultivars was reduced compared to the controls, yet it was much more pronounced in Voreas. We observed an increased accumulation of cinnamic acid on the seventh day only in the Gergana cultivar. A MicroNIR spectrometer was used for in vivo spectral measurements of cotyledons and the first two leaves. Differences in absorption spectra were observed among control, stressed, and recovered plants, across different days of stress, and between the studied cultivars. The most significant differences were in the 1300-1600 nm range, much smaller for Gergana than Voreas. Aquagrams of the two cultivars also reveal differences in their responses to low temperatures and changes in water molecular structure in the leaves. The errors of prediction for the days of chilling by using PLS models were from 0.96 to 1.14 days for independent validation, depending on the spectral data of different leaves used. Near-infrared spectroscopy and aquaphotomics can be used as additional tools for early detection of stress and investigation of low-temperature tolerance in cucumber cultivars. Photosynthesis ; Cultivar ; Spectroscopy ; Transpiration ; Water stress ; Spectral analysis ; Spectrometer ; Stomatal conductance Spectroscopy and Chemometric Analyses ; Light effects on plants ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Olamide Abiodun Daramola | Zolile Wiseman Dlamini | Talkmore Ngarivhume | Wakhiwe Mthandi Mthiyane | Xavier Siwe‐Noundou | Potlaki Foster Tseki
Source: Plasmonics (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Synthesis of Garlic-Stabilised AgNPs and Their Potential Application in Memory Devices Abstract In this study, we report the synthesis of silver nanoparticles (AgNPs) using different crude garlic aqueous extracts as stabilising agents and sodium borohydride as reducing agent. The crude garlic extract was obtained using the cold (maceration) and hydro-distillation extraction methods. The synthesised AgNPs and garlic-stabilised AgNPs (G-AgNPs) were characterised using various techniques. The formation of a surface plasmon resonance peak at 393 nm from the ultraviolet–visible (UV–Vis) spectroscopy indicates the synthesis of AgNPs. The fluorescence intensity displayed upon stabilisation with garlic extract depicts the passivation provided by the crude extracts. However, the fluorescence study showed that the hydrosol-stabilised AgNPs gave a higher emission intensity than the cold extract-stabilised ones. Variations in the concentration of hydrosol extract, AgNO 3 and NaBH 4 solutions showed that the fluorescence intensity of synthesised garlic-AgNPs was more enhanced at 0.063 g/ml of hydrosol, 0.3125 mM of AgNO 3 and 2.5 mM of NaBH 4 solution. The scanning electron microscope (SEM) results indicate the formation of spherically shaped NPs, whereas the result obtained from the transmission electron microscope (TEM) shows the formation of larger sizes of garlic-stabilised AgNPs compared to unstabilised AgNPs. The X-ray diffraction spectroscopy (XRD) result reveals lesser crystallinity for G-AgNPs than for AgNPs without garlic as a stabilising agent. The resistivity switching behaviour of optimised garlic-stabilised AgNPs with Cu and ITO electrodes exhibited remarkably similar I-V characteristics compared to uncoated AgNPs. This shows the potential use of garlic-stabilised AgNPs in memory devices. Materials science ; Nanotechnology Quantum Dots Synthesis And Properties ; Nanoparticles: synthesis and applications ; Dielectric properties of ceramics
Authors: Mason Hooten | N. Sanjeeva Murthy | Nityananda Pal | Sagar D. Khare | Adam J. Gormley | Meenakshi Dutt
Source: The Journal of Chemical Physics (article, 2025-04-03)
Field: Materials Science — Materials Chemistry

Martini 3 coarse-grained model of enzymes: Framework with validation by all-atom simulations and x-ray diffraction measurements Recent experiments have shown that complexation with a stabilizing compound can preserve enzyme activity in harsh environments. Such complexation is believed to be driven by noncovalent interactions at the enzyme surface, including hydrophobicity and electrostatics. Molecular modeling of these interactions is costly at the all-atom scale due to the long time scales and large particle counts needed to characterize binding. Protein structure at the scale of amino acid residues is parsimoniously represented by a coarse-grained model in which one particle represents several atoms, significantly reducing the cost of simulation. Coarse-grained models may then be used to generate reduced surface descriptions to underlie detailed theories of surface adhesion. In this study, we present two coarse-grained enzyme models-lipase and dehalogenase-that have been prepared using the Martini 3 top-down modeling framework. We simulate each enzyme in aqueous solution and calculate the statistics of protein surface features and shape descriptors. The values from the coarse-grained data are compared with the same calculations performed on all-atom reference systems, revealing key similarities of surface chemistry at the two scales. Structural measures are calculated from the all-atom reference systems and compared with estimates from small-angle x-ray scattering experiments, with good agreement between the two. The described procedures of modeling and analysis comprise a framework for the development of coarse-grained models of protein surfaces with validation to experiment. Electrostatics ; Surface (topology) ; Chemistry ; Scale (ratio) ; Atom (system on chip) ; Molecular dynamics ; Biological system ; Particle (ecology) ; Statistical physics ; Chemical physics ; Computational chemistry ; Crystallography ; Physics ; Physical chemistry ; Computer science ; Mathematics ; Geometry ; Quantum mechanics Enzyme Structure and Function ; Protein Structure and Dynamics ; Enzyme Production and Characterization
Authors: Nikita Levin | Cemil Can Saylan | Joel Lapin | Yana Demyanenko | Kevin Yang | John D. Sidda | Alexey I. Nesvizhskii | Mathias Wilhelm | Shabaz Mohammed
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-06-01)
Field: Chemistry — Spectroscopy

Integrating Alternative Fragmentation Techniques into Standard LC-MS Workflows Using a Single Deep Learning Model Enhances Proteome Coverage We built and characterised a mass spectrometer capable of performing CID (both beam type and resonant type), UVPD, EID and ECD in an automated fashion during an LCMS type experiment. We exploited this ability to generate large datasets through multienzyme deep proteomics experiments for characterisation of these activation techniques. As a further step, motivated by the complexity generated by these dissociation techniques, we developed a single Prosit deep learning model for fragment ion intensity prediction covering all of these techniques. The generated model has been made publicly available and has been utilised in FragPipe within its MSBooster module. Rescoring allowed both data-dependent acquisition (DDA) and data-independent acquisition (DIA) to achieve on average more than 10% increase in protein identifications across all dissociation techniques and enzymatic digestions. We demonstrate that these alternative fragmentation approaches can now be used within standard data analysis pipelines and can produce data competitive to CID in terms of efficiency, but in the cases of EID and UVPD with far richer and more comprehensive spectra. Workflow ; Fragmentation (computing) ; Computer science ; Proteome ; Computational biology ; Chemistry ; Database ; Biology ; Biochemistry ; Operating system Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Lili Kong | Xinna Yu | Qifang Sun | Meizhen Huang | Tianyuan Liu | Jie Chen
Source: Polymers (article, 2025-06-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Sensitive and Stable NCF/GO/Au@Ag SERS Substrate for Trace Detection of Polycyclic Aromatic Hydrocarbons Polycyclic aromatic hydrocarbons (PAHs) have attracted significant attention due to their severe threats to both ecological systems and human health. In this paper, a high-performance surface-enhanced Raman spectroscopy (SERS) substrate based on NCF/GO/Au@Ag nanocomposites was developed, which enabled sensitive and stable detection of PAHs. The NCF/GO/Au@Ag substrate synergistic utilizes the localized surface plasmon resonance (LSPR) effect of Au@Ag core-shell nanorods and the additional interfacial charge transfer provided by graphene oxide (GO) to exhibit extremely high sensitivity. And the three-dimensional fibrous network of nanocellulose (NCF) improved nanoparticle dispersion uniformity. Combined finite element simulations and experimental studies verified that the dual plasmonic resonances (512 nm and 772 nm) of Au@Ag nanorods optimally match 785 nm excitation, yielding an enhancement factor of 5.21 × 10<sup>5</sup>. GO integration enhanced Raman signals by 1.68-fold through interfacial charge transfer, while the introduction of NCF reduced the signal relative standard deviation (RSD) from 36.88% to 4.29%. The NCF/GO/Au@Ag substrate achieved a detection limit of 10 μg/L for PAHs, demonstrating exceptional sensitivity and reproducibility. Substrate (aquarium) ; TRACE (psycholinguistics) ; Nanotechnology ; Environmental chemistry ; Chemistry ; Materials science ; Geology Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: J. Christensen | Mette Marie Toldam-Andersen | Hans Jørgen Lyngs Jørgensen | Nikola Mićić | Nanna Bjarnholt | Mohammed Saddik Motawia | Mohammed Saddik Motawie | Mathilde Pizaine | Jan Günther | Louise Kjærulff | Dan Stærk | Meike Burow | Sara Thodberg | Elizabeth Heather Jakobsen Neilson
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-28)
Field: Chemistry — Analytical Chemistry

FMO and CYP monooxygenase families determine the metabolic flux of hydroxylated tryptamine derivatives in barley ( <i>Hordum vulgare</i> ) following pathogen infection Abstract To counteract pathogenic microorganisms, plants execute a complex resistance response that includes major metabolic reprogramming and production of bioactive defensive compounds. Barley ( Hordeum vulgare ) is a major cereal crop, but suffers significant yield losses due to pathogen attack every year. Here we use an untargeted metabolomic approach to assess the diversity and shifts in key barley metabolites produced in response to Pyrenophora teres f. teres infection, a hemibiotrophic fungal pathogen and causal agent of net blotch disease. Tryptophan-derived compounds, including tryptamine, serotonin, and a novel indole alkaloid – 2-oxo-tryptamine (2OT) – were among the most significantly induced and abundant compounds, with mass spectrometry imaging revealing that these metabolites accumulate at the site of infection. A transcriptomic approach identified a flavin-containing monooxygenase (FMO), which was functionally characterized as a 2-oxo-tryptamine synthase (2OTS). In addition, a cytochrome P450 (CYP71P10) was characterized as a tryptamine-5-hydroxylase, responsible for serotonin biosynthesis. These characterized genes are tightly co-expressed with genes involved in tryptophan biosynthesis and signify a major metabolic flux towards indolic compounds after infection, potentially serving as bioactive phytoalexins. Additional microbial interactions using a biotrophic fungus ( Blumeria hordei ; powdery mildew), a hemibiotrophic bacterium ( Pseudomonas syringae ), and alternative cultivars suggest that these pathways represent a generally activated resistance response in barley. These results provide new insights within the barley defense response relevant for the development of disease resistant traits in cereal crops. Tryptamine ; Monooxygenase ; Flux (metallurgy) ; Biology ; Pathogen ; Hordeum vulgare ; Enzyme ; Biochemistry ; Genetics ; Chemistry ; Botany ; Cytochrome P450 ; Poaceae ; Organic chemistry Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Mycotoxins in Agriculture and Food
Authors: Lingyu Zhang | Yijia Wang | Xiaoyan Zheng
Source: Chemistry - An Asian Journal (review, 2025-06-06)
Field: Materials Science — Biomaterials

Kinetic Network Models to Elucidate the Kinetic‐Controlled Molecular Assembly Processes Organic molecular assembly is a fundamental protocol for constructing organic functional materials at time and length scales beyond individual molecules. Following a bottom-up strategy, organic nanostructures with diverse morphologies and specific functionalities could be obtained. However, the flexible conformations and the cooperative interplay of different noncovalent interactions, lead to countless kinetically metastable states and make the precise prediction of assembled nanostructures extremely challenging. In this review, the theoretical backgrounds and a general theoretical protocol of kinetic network models (KNMs) are first introduced. Then, the molecular assembly mechanism and its regulation are presented for various molecular assembly systems ranging from small molecules (e.g., surfactants, lipids, metal complexes, and ice nuclei) to block copolymers and patchy particles, and further to peptides. For each assembly system, the distribution of metastable structures and the kinetically assembled pathways of the assembly process, as well as the relationship between kinetic pathways preferences and the finally assembled nanostructures are presented. Therefore, it is crucial for a deeper understanding of assembly mechanism and it paves an effective way for the precise control of assembled nanostructures kinetically, which benefits the fabrication of advanced organic functional materials. Kinetic control ; Nanotechnology ; Nanostructure ; Metastability ; Self-assembly ; Molecule ; Materials science ; Chemical physics ; Molecular dynamics ; Kinetic energy ; Supramolecular chemistry ; Mechanism (biology) ; Kinetics ; Organic molecules ; Chemistry ; Computational chemistry ; Physics ; Catalysis Supramolecular Self-Assembly in Materials ; Advanced Polymer Synthesis and Characterization ; Pickering emulsions and particle stabilization
Authors: Jiaoling Wang | Zhengyin Ding | Xinxin Wu | Xindong Wang | Hao Li | Minchen Zhu | Xinai Zhang
Source: Foods (article, 2025-11-04)
Field: Chemistry — Electrochemistry

Tracking Cadmium Transfer from Soil to Cup: An Electrochemical Sensing Strategy Based on Bi3+-Rich MOFs for Tea Safety Monitoring Tea is one of the most widely consumed beverages worldwide, yet increasing environmental cadmium (Cd<sup>2+</sup>) contamination poses a serious threat to consumer safety. Understanding the migration pathway of Cd<sup>2+</sup> from contaminated soils through tea plants into brewed infusions is essential for comprehensive risk assessment across the entire tea supply chain. However, conventional analytical methods for Cd<sup>2+</sup> detection are often time-consuming, labor-intensive, and unsuitable for rapid or on-site monitoring. In this study, we developed a facile, sensitive, and selective electrochemical sensing platform based on a Bi<sup>3+</sup>-rich metal-organic framework (MOF(Bi)) for reliable Cd<sup>2+</sup> quantification in various tea-related matrices. The MOF(Bi) was synthesized via a solvothermal method and directly immobilized onto a glassy carbon electrode (GCE) in a one-step modification process. To enhance Cd<sup>2+</sup> preconcentration, cysteine was introduced as a complexing agent, while Nafion was employed to stabilize the sensing interface and improve reproducibility. The resulting Nafion/cys/MOF(Bi)/GCE sensor exhibited excellent sensitivity with a wide linear range from 0.2 and 25 μg/L, a low detection limit of 0.18 μg/L (<i>S</i>/<i>N</i> = 3), high selectivity against common interfering ions, and good stability. This platform enabled accurate tracking of Cd<sup>2+</sup> transfer from polluted garden soil to raw tea leaves and finally into tea infusions, showing strong correlation with ICP-MS results. Our strategy not only offers a practical tool for on-site food safety monitoring but also provides new insights into heavy metal transfer behavior during tea production and consumption. Contamination ; Cadmium ; Detection limit ; Raw material ; Electrochemical gas sensor ; Food safety ; Electrochemistry ; Environmental monitoring Electrochemical Analysis and Applications ; Heavy metals in environment ; Analytical chemistry methods development
Authors: Francesco Strata | Luca Migliori | Nour Gebran | Nicolina Guarino | Giorgio Colombo | Sara Pezzuolo | Emiliano Luzietti
Source: Preprints.org (review, 2025-04-24)
Field: Materials Science — Materials Chemistry

Quantum Machine Learning Early Opportunities for the Energy Industry: a Systematic Review Quantum computing innovations have garnered significant attention for their potential to revolutionize industries, with the energy sector being one of the most promising areas for application. As global energy demand increases and sustainability becomes more critical, computational technologies offer groundbreaking solutions for energy production, storage, and distribution. In this landscape, quantum computing plays a crucial role in unlocking the full potential of artificial intelligence (AI) and machine learning (ML), as the research and development in the quantum machine learning (QML) field grows constantly. In this paper, we present a systematic review of early quantum machine learning applications within the energy industry value chain. We analyze and discuss various use cases in the energy sector, thoroughly examining each one to understand its potential applications and impact. We then evaluate these early-stage quantum applications to determine their feasibility and benefits, offering insights into their relevance and effectiveness in the context of the industry’s evolving landscape. This is done by introducing a novel framework: the Assessment Model for Innovation Management (AMIM). Our research highlights the opportunities quantum innovations present for the energy sector and offers actionable insights into which applications are the best investments and why. Overall, the feasibility and technological maturity of QML use cases are still in the early stages, though their market compatibility and potential benefits are relatively high for most of them. This indicates that while quantum machine learning holds immense potential, further development is necessary to fully realize its benefits in the energy sector. Energy (signal processing) ; Systematic review ; Quantum ; Physics ; MEDLINE ; Political science ; Quantum mechanics Machine Learning in Materials Science ; Market Dynamics and Volatility
Authors: Ashraf M. El. Hashani | Haytham M. Abuissa | Ritaj A. Riheel
Source: Scientiae Radices (article, 2025-07-01)
Field: Chemistry — Spectroscopy

Benzalkonium Chloride Micellization: Salt and Temperature Dependence (Conductivity, Surface Tension) The aggregation behavior of cationic surfactant (Benzalkonium chloride (BKC)), in the absence and presence of KCl and MgSO4 in aqueous media, was investigated at room temperature by surface tension measurements and at different temperatures by conductivity measurements. Critical micelle concentration (CMC), micelle ionization degree (α), the thermodynamic parameters (standard Gibb's energy of micellization (ΔGomic), enthalpy of micellization (ΔHomic), and entropy of micellization (ΔSomic)), and interfacial parameters were determined. In addition, the geometry of formed micelles was investigated. The effect of magnesium sulfate on BKC micellization was more pronounced compared to potassium chloride. At all temperatures, BKC's CMC values were lower with magnesium sulfate than with potassium chloride. Similarly, the CMC and α values were directly proportional to temperature. Benzalkonium chloride ; Surface tension ; Salt (chemistry) ; Conductivity ; Chloride ; Materials science ; Chemistry ; Chemical engineering ; Thermodynamics of micellization ; Thermodynamics ; Chromatography ; Critical micelle concentration ; Micelle ; Physical chemistry ; Organic chemistry ; Aqueous solution ; Physics Analytical Chemistry and Chromatography ; Environmental Chemistry and Analysis ; Analytical Chemistry and Sensors