jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sander J. W. Vonk | J. J. Erik Maris | Ayla J. H. Dekker | Jur W. de Wit | Thomas P. van Swieten | Ario Cocina | Freddy T. Rabouw
Source: ACS Nano (article, 2024-10-05)
Field: Materials Science — Materials Chemistry

Rise and Decay of Photoluminescence in Upconverting Lanthanide-Doped Nanocrystals Nanocrystals (NCs) doped with lanthanides are capable of efficient photon upconversion, i.e., absorbing long-wavelength light and emitting shorter-wavelength light. The internal processes that enable upconversion are a complex network of electronic transitions within and energy transfer between dopant centers. In this work, we study the rise and decay dynamics of upconversion emission from β-NaYF<sub>4</sub> NCs codoped with Er<sup>3+</sup> and Yb<sup>3+</sup>. The rise dynamics of the red and green upconverted emissions are nonlinear, reflecting the nonlinear nature of upconversion and revealing the mechanisms that populate the emitting states. The excited-state decay dynamics are nonexponential. We unravel the underlying decay pathways using photonic experiments. These reveal the contributions of different upconversion pathways visually, as each pathway exhibits a distinct response to systematic variation of the local density of optical states. Moreover, the effect of the local density of optical states on core-only NCs is qualitatively different from core-shell NCs. This is due to the different balance between feeding and decay of the electronic levels that produce upconverted emission. The understanding of the upconversion dynamics provided here could lead to better imaging and sensing methods relying on upconversion lifetimes or guide the rational optimization of the dopant concentrations for brighter upconversion. Photoluminescence ; Nanocrystal ; Lanthanide ; Materials science ; Doping ; Nanotechnology ; Photon upconversion ; Optoelectronics ; Chemistry ; Ion Luminescence Properties of Advanced Materials ; Lanthanide and Transition Metal Complexes ; Quantum Dots Synthesis And Properties
Authors: Lyndon Emsley
Source: Faraday Discussions (article, 2024-09-06)
Field: Chemistry — Spectroscopy

Spiers Memorial Lecture: NMR crystallography Chemical function is directly related to the spatial arrangement of atoms. Consequently, the determination of atomic-level three-dimensional structures has transformed molecular and materials science over the past 60 years. In this context, solid-state NMR has emerged to become the method of choice for atomic-level characterization of complex materials in powder form. In the following we present an overview of current methods for chemical shift driven NMR crystallography, illustrated with applications to complex materials. Chemistry ; Crystallography ; Materials science Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Molecular spectroscopy and chirality
Authors: Minghao Huang | Yu Tang | Zhiping Tan | Jinchang Ren | Yong He | Huasheng Huang
Source: Infrared Physics & Technology (article, 2024-11-09)
Field: Chemistry — Analytical Chemistry

Detection of black tea fermentation quality based on optimized deep neural network and hyperspectral imaging Hyperspectral imaging ; Black tea ; Artificial neural network ; Remote sensing ; Computer science ; Quality (philosophy) ; Artificial intelligence ; Geology ; Food science ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Remote Sensing and Land Use
Authors: Gul Muneer | Ciao‐Syuan Chen | Yu‐Ju Chen
Source: PROTEOMICS (review, 2024-12-18)
Field: Chemistry — Spectroscopy

Advancements in Global Phosphoproteomics Profiling: Overcoming Challenges in Sensitivity and Quantification Protein phosphorylation introduces post-genomic diversity to proteins, which plays a crucial role in various cellular activities. Elucidation of system-wide signaling cascades requires high-performance tools for precise identification and quantification of dynamics of site-specific phosphorylation events. Recent advances in phosphoproteomic technologies have enabled the comprehensive mapping of the dynamic phosphoproteomic landscape, which has opened new avenues for exploring cell type-specific functional networks underlying cellular functions and clinical phenotypes. Here, we provide an overview of the basics and challenges of phosphoproteomics, as well as the technological evolution and current state-of-the-art global and quantitative phosphoproteomics methodologies. With a specific focus on highly sensitive platforms, we summarize recent trends and innovations in miniaturized sample preparation strategies for micro-to-nanoscale and single-cell profiling, data-independent acquisition mass spectrometry (DIA-MS) for enhanced coverage, and quantitative phosphoproteomic pipelines for deep mapping of cell and disease biology. Each aspect of phosphoproteomic analysis presents unique challenges and opportunities for improvement and innovation. We specifically highlight evolving phosphoproteomic technologies that enable deep profiling from low-input samples. Finally, we discuss the persistent challenges in phosphoproteomic technologies, including the feasibility of nanoscale and single-cell phosphoproteomics, as well as future outlooks for biomedical applications. Phosphoproteomics ; Computational biology ; Profiling (computer programming) ; Proteomics ; Biology ; Computer science ; Protein phosphorylation ; Phosphorylation ; Cell biology ; Protein kinase A ; Genetics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Biosensors and Analytical Detection
Authors: Anatoly Sorokin | Stanislav I. Pekov | Denis S. Zavorotnyuk | Mariya M. Shamraeva | Denis S. Bormotov | Igor Popov
Source: Mass Spectrometry Reviews (review, 2024-04-26)
Field: Chemistry — Spectroscopy

Modern machine‐learning applications in ambient ionization mass spectrometry This article provides a comprehensive overview of the applications of methods of machine learning (ML) and artificial intelligence (AI) in ambient ionization mass spectrometry (AIMS). AIMS has emerged as a powerful analytical tool in recent years, allowing for rapid and sensitive analysis of various samples without the need for extensive sample preparation. The integration of ML/AI algorithms with AIMS has further expanded its capabilities, enabling enhanced data analysis. This review discusses ML/AI algorithms applicable to the AIMS data and highlights the key advancements and potential benefits of utilizing ML/AI in the field of mass spectrometry, with a focus on the AIMS community. Chemistry ; Mass spectrometry ; Ambient ionization ; Ionization ; Ion-mobility spectrometry–mass spectrometry ; Chromatography ; Chemical ionization ; Analytical Chemistry (journal) ; Electrospray ionization ; Sample preparation in mass spectrometry ; Ion ; Organic chemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Elsayed M. Atwa | Shaomin Xu | Ahmed K. Rashwan | Asem M. Abdelshafy | Gamal ElMasry | Salim S. Al‐Rejaie | Hong Xu | Hongjian Lin | Jinming Pan
Source: Foods (review, 2024-11-07)
Field: Chemistry — Analytical Chemistry

Advances in Emerging Non-Destructive Technologies for Detecting Raw Egg Freshness: A Comprehensive Review Eggs are a rich food source of proteins, fats, vitamins, minerals, and other nutrients. However, the egg industry faces some challenges such as microbial invasion due to environmental factors, leading to damage and reduced usability. Therefore, detecting the freshness of raw eggs using various technologies, including traditional and non-destructive methods, can overcome these challenges. As the traditional methods of assessing egg freshness are often subjective and time-consuming, modern non-destructive technologies, including near-infrared (NIR) spectroscopy, Raman spectroscopy, fluorescence spectroscopy, computer vision (color imaging), hyperspectral imaging, electronic noses, and nuclear magnetic resonance, have offered objective and rapid results to address these limitations. The current review summarizes and discusses the recent advances and developments in applying non-destructive technologies for detecting raw egg freshness. Some of these technologies such as NIR spectroscopy, computer vision, and hyperspectral imaging have achieved an accuracy of more than 96% in detecting egg freshness. Therefore, this review provides an overview of the current trends in the state-of-the-art non-destructive technologies recently utilized in detecting the freshness of raw eggs. This review can contribute significantly to the field of emerging technologies in this research track and pique the interests of both food scientists and industry professionals. Hyperspectral imaging ; Emerging technologies ; Computer science ; Food products ; Usability ; Environmental science ; Biochemical engineering ; Nanotechnology ; Artificial intelligence ; Materials science ; Engineering ; Biology ; Human–computer interaction ; Food science Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Varun Dolia | Halleh B. Balch | Sahil Dagli | Sajjad Abdollahramezani | Hamish Carr Delgado | Parivash Moradifar | Kai Chang | Ariel Stiber | Fareeha Safir | Mark Lawrence | Jack Hu | Jennifer A. Dionne
Source: Nature Nanotechnology (article, 2024-07-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Very-large-scale-integrated high quality factor nanoantenna pixels Resonator ; Q factor ; Optics ; Optoelectronics ; Photonic crystal ; Plasmon ; Materials science ; Scale factor (cosmology) ; Spectrometer ; Physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Luisa Campagnolo | Valentina Lacconi | Joanna Filippi | Eugenio Martinelli
Source: Frontiers in Toxicology (article, 2024-09-18)
Field: Materials Science — Materials Chemistry

Twenty years of in vitro nanotoxicology: how AI could make the difference More than two decades ago, the advent of Nanotechnology has marked the onset of a new and critical field in science and technology, highlighting the importance of multidisciplinary approaches to assess and model the potential human hazard of newly developed advanced materials in the nanoscale, the nanomaterials (NMs). Nanotechnology is, by definition, a multidisciplinary field, that integrates knowledge and techniques from physics, chemistry, biology, materials science, and engineering to manipulate matter at the nanoscale, defined as anything comprised between 1 and 100 nm. The emergence of nanotechnology has undoubtedly led to significant innovations in many fields, from medical diagnostics and targeted drug delivery systems to advanced materials and energy solutions. However, the unique properties of nanomaterials, such as the increased surface to volume ratio, which provides increased reactivity and hence the ability to penetrate biological barriers, have been also considered as potential risk factors for unforeseen toxicological effects, stimulating the scientific community to investigate to which extent this new field of applications could pose a risk to human health and the environment. Nanotechnology ; Multidisciplinary approach ; Nanotoxicology ; Nanomedicine ; Engineering ethics ; Risk analysis (engineering) ; Engineering ; Medicine ; Materials science ; Nanoparticle ; Political science Nanoparticles: synthesis and applications ; 3D Printing in Biomedical Research
Authors: Simon J. L. Billinge | Thomas Proffen
Source: Acta Crystallographica Section A Foundations and Advances (article, 2024-01-26)
Field: Materials Science — Materials Chemistry

Machine learning in crystallography and structural science An overview of the virtual collection on machine learning (ML) in crystallography and structural science, as represented in Acta Crystallographica Sections A, B and D, IUCrJ and Journal of Synchrotron Radiation, is presented. Some terms and concepts related to artificial intelligence and machine learning are briefly introduced and described, and a short history of ML in structural science as it appeared in these IUCr journals is given to whet the appetite for the rest of the collection. Computer science ; Artificial intelligence ; Synchrotron radiation ; Optics ; Physics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Enzyme Structure and Function
Authors: Ivan Khokhlov | Leonid Legashev | Irina Bolodurina | Alexander Shukhman | Daniil E Shoshin | Svetlana Kolesnik
Source: Toxics (article, 2024-10-15)
Field: Materials Science — Materials Chemistry

Prediction of Dynamic Toxicity of Nanoparticles Using Machine Learning Predicting the toxicity of nanoparticles plays an important role in biomedical nanotechnologies, in particular in the creation of new drugs. Safety analysis of nanoparticles can identify potentially harmful effects on living organisms and the environment. Advanced machine learning models are used to predict the toxicity of nanoparticles in a nutrient solution. In this article, we performed a comparative analysis of the current state of research in the field of nanoparticle toxicity analysis using machine learning methods; we trained a regression model for predicting the quantitative toxicity of nanoparticles depending on their concentration in the nutrient solution at a fixed point in time with the achieved metrics values of MSE = 2.19 and RMSE = 1.48; we trained a multi-class classification model for predicting the toxicity class of nanoparticles depending on their concentration in the nutrient solution at a fixed point in time with the achieved metrics values of Accuracy = 0.9756, Recall = 0.9623, F1-Score = 0.9640, and Log Loss = 0.1855. As a result of the analysis, we concluded the good predictive ability of the trained models. The optimal dosages for the nanoparticles under study were determined as follows: ZnO = 9.5 × 10<sup>-5</sup> mg/mL; Fe<sub>3</sub>O<sub>4</sub> = 0.1 mg/mL; SiO<sub>2</sub> = 1 mg/mL. The most significant features of predictive models are the diameter of the nanoparticle and the nanoparticle concentration in the nutrient solution. Nanoparticle ; Toxicity ; Machine learning ; Computer science ; Biological system ; Mathematics ; Chemistry ; Artificial intelligence ; Materials science ; Nanotechnology ; Biology Nanoparticles: synthesis and applications ; Advanced Computational Techniques in Science and Engineering
Authors: Chen Ying | Si Li | Jia Jia | Chuanduo Sun | Enzhong Cui | Yun‐Yan Xu | Fangchao Shi | Anfu Tang
Source: Food Chemistry X (article, 2024-09-02)
Field: Chemistry — Analytical Chemistry

FT-NIR combined with machine learning was used to rapidly detect the adulteration of pericarpium citri reticulatae (chenpi) and predict the adulteration concentration Pericarpium citri reticulatae (PCR) has been used as a food and spice for many years and is known for its rich nutritional content and unique aroma. However, price increases are often accompanied by adulteration. In this study, two kinds of adulterants (Orange peel-OP and Mandarin Rind-MR) were identified by chromaticity analysis, FT-NIR and machine learning algorithm, and the doping concentration was predicted quantitatively. The results show that colorimetric analysis cannot completely differentiate between PCR and adulterants. Using spectral preprocessing combined with machine learning algorithms, PCR and two adulterants were successfully distinguished, with classification accuracy reaching 99.30 % and 98.64 % respectively. After selecting characteristic wavelengths, the R<sup>2</sup> <sub>P</sub> of the adulterated quantitative model is greater than 0.99. Generally, this study proposes to use FT-NIR to study the adulteration of PCR for the first time, which fills the technical gap in the adulteration research of PCR, and provides an important method to solve the increasingly serious adulteration problem of PCR. Aroma ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Chemistry ; Orange (colour) ; Food science ; Chromatography ; Computer science Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Advanced Chemical Sensor Technologies
Authors: Shuya Zhu | Dingshun Yan | Yong Zhang | Liuliu Han | Dierk Raabe | Zhiming Li
Source: Nature Communications (article, 2024-08-22)
Field: Materials Science — Materials Chemistry

Strong and ductile Resinvar alloys with temperature- and time-independent resistivity Materials with well-defined electrical resistivity that does not change with temperature or time are important in robotics, communication and automation. However, the challenge of designing such materials has remained elusive due to the temperature-dependent electron-phonon scattering. Moreover, resistive electrical conductors used in flexible and mobile systems under high mechanical loads must possess both high strength and ductility. Achieving such multi-properties presents a fundamental challenge. Here, we solve this problem by combining multicomponent alloy design with atomic-scale chemistry tuning. We term the resultant material 'Resinvar' alloy, due to its invariable resistivity (148 μΩ·cm) over wide temperature ranges from room temperature to 723 K. The alloy also has high tensile strength (948 MPa) at large tensile elongation (53%). The distorted lattice, chemical short-range order and ordered coherent nanoprecipitates in the material enable the invariant resistivity via promoting temperature-independent inelastic electron scattering, and contribute to the excellent strength-ductility synergy by manipulating dislocation slip. Electrical resistivity and conductivity ; Materials science ; Ultimate tensile strength ; Alloy ; Scattering ; Resistive touchscreen ; Annealing (glass) ; Atmospheric temperature range ; Ductility (Earth science) ; Phonon ; Condensed matter physics ; Electrical conductor ; Composite material ; Computer science ; Electrical engineering ; Optics ; Thermodynamics ; Creep ; Physics MXene and MAX Phase Materials ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Supriya Atta | Aidan J. Canning | Tuan Vo‐Dinh
Source: The Analyst (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A simple low-cost flexible plasmonic patch based on spiky gold nanostars for ultra-sensitive SERS sensing Recently, transparent and flexible surface-enhanced Raman scattering (SERS) substrates have received great interest for direct point-of-care detection of analytes on irregular nonplanar surfaces. In this study, we proposed a simple cost-effective strategy to develop a flexible SERS patch utilizing multibranched sharp spiked gold nanostars (GNS) decorated on a commercially available adhesive Scotch Tape for achieving ultra-high SERS sensitivity. The experimental SERS measurements were correlated with theoretical finite element modeling (FEM), which indicates that the GNS having a 2.5 nm branch tip diameter (GNS-4) exhibits the strongest SERS enhancement. Using rhodamine 6G (R6G) as a model analyte, the SERS performance of the flexible SERS patch exhibited a minimum detection limit of R6G as low as 1 pM. The enhancement factor of the SERS patch with GNS-4 was calculated as 6.2 × 10<sup>8</sup>, which indicates that our flexible SERS substrate has the potential to achieve ultra-high sensitivity. The reproducibility was tested with 30 different spots showing a relative standard deviation (RSD) of SERS intensity of about 5.4%, indicating good reproducibility of the SERS platform. To illustrate the usefulness of the flexible SERS sensor patch, we investigated the detection of a carcinogenic compound crystal violet (CV) on fish scales, which is often used as an effective antifungal agent in the aquaculture industry. The results realized the trace detection of CV with the minimum detection limit as low as 1 pM. We believe that our transparent, and flexible SERS patch based on GNS-4 has potential as a versatile, low-cost platform for real-world SERS sensing applications on nonplanar surfaces. Plasmon ; Raman scattering ; Materials science ; Nanotechnology ; Simple (philosophy) ; Raman spectroscopy ; Optoelectronics ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced Nanomaterials in Catalysis
Authors: Wei-Liang Lin | Yao Yuan | Lulu Xu | Wei Wang
Source: Molecules (article, 2024-04-19)
Field: Materials Science — Polymers and Plastics

Recent Progress in Two-Dimensional Nanomaterials for Flame Retardance and Fire-Warning Applications Graphene-like 2D nanomaterials, such as graphene, MXene, molybdenum disulfide, and boron nitride, present a promising avenue for eco-friendly flame retardants. Their inherent characteristics, including metal-like conductivity, high specific surface area, electron transport capacity, and solution processability, make them highly suitable for applications in both structural fire protection and fire alarm systems. This review offers an up-to-date exploration of advancements in flame retardant composites, utilizing pristine graphene-like nanosheets, versatile graphene-like nanosheets with multiple functions, and collaborative systems based on these nanomaterials. Moreover, graphene-like 2D nanomaterials exhibit considerable potential in the development of early fire alarm systems, enabling timely warnings. This review provides an overview of flame-retarding and fire-warning mechanisms, diverse multifunctional nanocomposites, and the evolving trends in the development of fire alarm systems anchored in graphene-like 2D nanomaterials and their derivatives. Ultimately, the existing challenges and prospective directions for the utilization of graphene-like 2D nanomaterials in flame retardant and fire-warning applications are put forward. Graphene ; Nanomaterials ; Nanotechnology ; Materials science ; Fire retardant ; Molybdenum disulfide ; Boron nitride ; Composite material Flame retardant materials and properties ; Graphene research and applications ; Covalent Organic Framework Applications
Authors: Sheng Chen | Sen Zhang | Xiaoyu Fang | Liang Lin | Huiying Zhao | Yuedong Yang
Source: Nature Communications (article, 2024-10-11)
Field: Materials Science — Materials Chemistry

Protein complex structure modeling by cross-modal alignment between cryo-EM maps and protein sequences Cryo-electron microscopy (cryo-EM) technique is widely used for protein structure determination. Current automatic cryo-EM protein complex modeling methods mostly rely on prior chain separation. However, chain separation without sequence guidance often suffers from errors caused by cross-chain interaction or noise densities, which would accumulate and mislead the subsequent steps. Here, we present EModelX, a fully automated cryo-EM protein complex structure modeling method, which achieves sequence-guiding modeling through cross-modal alignments between cryo-EM maps and protein sequences. EModelX first employs multi-task deep learning to predict Cα atoms, backbone atoms, and amino acid types from cryo-EM maps, which is subsequently used to sample Cα traces with amino acid profiles. The profiles are then aligned with protein sequences to obtain initial structural models, which yielded an average RMSD of 1.17 Å in our test set, approaching atomic-level precision in recovering PDB-deposited structures. After filling unmodeled gaps through sequence-guiding Cα threading, the final models achieved an average TM-score of 0.808, outperforming the state-of-the-art method. The further combination with AlphaFold can improve the average TM-score to 0.911. Analyzes conducted by comparing some EModelX-built models and PDB structures highlight its potential to improve PDB structures. EModelX is accessible at https://bio-web1.nscc-gz.cn/app/EModelX . Protein structure ; Modal ; Computational biology ; Computer science ; Biology ; Chemistry ; Biochemistry Enzyme Structure and Function ; Genomics and Phylogenetic Studies ; Bacteriophages and microbial interactions