jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrew C. Gill | Srishti Gaur | Clay Sneller | D. Drewry
Source: Frontiers in Plant Science (article, 2024-10-31)
Field: Chemistry — Analytical Chemistry

Utilizing VSWIR spectroscopy for macronutrient and micronutrient profiling in winter wheat This study explores the use of leaf-level visible-to-shortwave infrared (VSWIR) reflectance observations and partial least squares regression (PLSR) to predict foliar concentrations of macronutrients (nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur), micronutrients (boron, copper, iron, manganese, zinc, molybdenum, aluminum, and sodium), and moisture content in winter wheat. A total of 360 fresh wheat leaf samples were collected from a wheat breeding population over two growing seasons. These leaf samples were used to collect VSWIR reflectance observations across a spectral range spanning 350 to 2,500 nm. These samples were then processed for nutrient composition to allow for the examination of the ability of reflectance to accurately model diverse chemical components in wheat foliage. Models for each nutrient were developed using a rigorous cross-validation methodology in conjunction with three distinct component selection methods to explore the trade-offs between model complexity and performance in the final models. We examined absolute minimum predicted residual error sum of squares (PRESS), backward iteration over PRESS, and Van der Voet's randomized <i>t</i>-test as component selection methods. In addition to contrasting component selection methods for each leaf trait, the importance of spectral regions through variable importance in projection scores was also examined. In general, the backward iteration method provided strong model performance while reducing model complexity relative to the other selection methods, yielding <i>R</i> <sup>2</sup> [relative percent difference (RPD), root mean squared error (RMSE)] values in the validation dataset of 0.84 (2.45, 6.91), 0.75 (1.97, 18.67), 0.78 (2.13, 16.49), 0.66 (1.71, 17.13), 0.68 (1.75, 14.51), 0.66 (1.72, 12.29), and 0.84 (2.46, 2.20) for nitrogen, calcium, magnesium, sulfur, iron, zinc, and moisture content on a wet basis, respectively. These model results demonstrate that VSWIR reflectance in combination with modern statistical modeling techniques provides a powerful high throughput method for the quantification of a wide range of foliar nutrient contents in wheat crops. This work has the potential to advance rapid, precise, and nondestructive field assessments of nutrient contents and deficiencies for precision agricultural management and to advance breeding program assessments. Micronutrient ; Winter wheat ; Profiling (computer programming) ; Agronomy ; Biology ; Environmental science ; Chemistry ; Computer science Spectroscopy and Chemometric Analyses ; Isotope Analysis in Ecology ; Soil Geostatistics and Mapping
Authors: Joshua Davis | Sai Prasanna Chinthala | Chelsea Monty-Bromer | John M. Senko
Source: Environmental Microbiology Reports (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Electrochemical Detection of Carbon Steel Corrosion Induced by Fermentative Bacteria From Natural Gas Transmission Lines The metabolic potential and corrosive activities of a fermentative bacterial enrichment culture from a natural gas transmission line were characterised. Three metagenome-assembled genomes (MAGs) attributable to Cytobacillus, Lacrimispora and Staphylococcus spp. were obtained. These MAGs hosted genes involved in the fermentation of carbohydrates to organic acids, which was reflected in the acidification of the growth medium by the culture. To evaluate the corrosive activities of the culture, it was incubated in a split chamber-zero resistance ammetry (SC-ZRA) format. This involved deploying carbon steel coupons immersed in liquid medium in opposing chambers of an electrochemical cell. Measurement of current between the coupons indicated the extent and mechanism of corrosion. When the enrichment culture was added to one side of an SC-ZRA incubation with bicarbonate-buffered medium, pH change and corrosion were minimal. In bicarbonate-free medium, the culture acidified the medium, induced electron transfer from the uninoculated chamber to the inoculated chamber, and caused mass loss. These results indicate that fermenter-induced microbially influenced corrosion (MIC) is due to localised fluid acidification, inducing anodic reactions on the metal surface exposed to the microorganisms and mass loss of the non-exposed metal. Corrosion ; Electrochemistry ; Bacteria ; Natural gas ; Carbon fibers ; Chemistry ; Transmission (telecommunications) ; Biochemical engineering ; Materials science ; Biology ; Metallurgy ; Computer science ; Electrode ; Composite material ; Organic chemistry ; Telecommunications ; Engineering Corrosion Behavior and Inhibition ; Dental materials and restorations ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Deborah McGlynn | Nirina R. Andriamaharavo | Anthony J. Kearsley
Source: Journal of Mass Spectrometry (article, 2024-09-11)
Field: Chemistry — Spectroscopy

Improved Discrimination of Mass Spectral Isomers Using the High‐Dimensional Consensus Mass Spectral Similarity Algorithm This study employs a high-dimensional consensus mass spectral (HDCMS) similarity scoring technique to discriminate isomers collected using an electron ionization mass spectrometer. The HDCMS method was previously introduced and applied to the discrimination of mass spectra of constitutional isomers, methamphetamine and phentermine, collected with direct analysis real-time mass spectrometry (DART-MS). The method formulates the problem of discriminating mass spectra in a mathematical Hilbert space and is hence called "high dimensional." It requires replicate mass spectra to build a Gaussian model and evaluate the inner products between these functions. The resulting measurement variability is used as a signature by which to discriminate spectra. In this work, HDCMS is tested on electron impact ionization (EI) mass spectra of 7 terpene and terpene-related (C<sub>10</sub>H<sub>16</sub> and C<sub>10</sub>H<sub>14</sub>) isomers with experimental retention indices that differ by less than 30 and with traditional cosine similarity scores greater than 0.9, on a scale of 0 to 1, when compared with at least one other compound in the test set. Using identical instrument parameters, 15 replicate gas chromatography-electron ionization-mass spectrometry (GC-EI-MS) spectra of each isomer were collected and separated into distinct library and query sets. The HDCMS algorithm discriminated each isomer, indicating the method's potential. Because the method requires replicate measurements, observations from a simple heuristic study of the number of replicates required to discriminate these isomers is presented. The paper concludes with a discussion of compound discrimination using HDCMS and the benefits and drawbacks of applying the method to EI-MS data. Chemistry ; Mass spectrum ; Replicate ; Mass spectrometry ; Similarity (geometry) ; Electron ionization ; Analytical Chemistry (journal) ; Spectral line ; Ionization ; Algorithm ; Biological system ; Artificial intelligence ; Chromatography ; Pattern recognition (psychology) ; Statistics ; Mathematics ; Organic chemistry ; Computer science ; Physics Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Patrick Trimby | Mohammed Al-Mosawi | Maisoon Al‐Jawad | Stuart Micklethwaite | Zabeada Aslam | Aimo Winkelmann | Sandra Piazolo
Source: Ultramicroscopy (article, 2024-02-24)
Field: Materials Science — Biomaterials

The characterisation of dental enamel using transmission Kikuchi diffraction in the scanning electron microscope combined with dynamic template matching Enamel paint ; Transmission electron microscopy ; Materials science ; Scanning electron microscope ; Dental enamel ; Conventional transmission electron microscope ; Diffraction ; Scanning transmission electron microscopy ; Optics ; Nanotechnology ; Physics ; Composite material Calcium Carbonate Crystallization and Inhibition ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Yu Miyajima | Kotaro Ochiai | Shinya Fujii
Source: Molecules (article, 2024-04-02)
Field: Chemistry — Organic Chemistry

Design, Synthesis, and Evaluation of B-(Trifluoromethyl)phenyl Phosphine–Borane Derivatives as Novel Progesterone Receptor Antagonists We previously revealed that phosphine-boranes can function as molecular frameworks for biofunctional molecules. In the present study, we exploited the diversity of available phosphines to design and synthesize a series of <i>B</i>-(trifluoromethyl)phenyl phosphine-borane derivatives as novel progesterone receptor (PR) antagonists. We revealed that the synthesized phosphine-borane derivatives exhibited Log<i>P</i> values in a predictable manner and that the P-H group in the phosphine-borane was almost nonpolar. Among the synthesized phosphine-boranes, which exhibited PR antagonistic activity, <i>B</i>-(4-trifluoromethyl)phenyl tricyclopropylphosphine-borane was the most potent with an IC<sub>50</sub> value of 0.54 μM. A docking simulation indicated that the tricyclopropylphosphine moiety plays an important role in ligand-receptor interactions. These results support the idea that phosphine-boranes are versatile structural options in drug discovery, and the developed compounds are promising lead compounds for further structural development of next-generation PR antagonists. Borane ; Phosphine ; Boranes ; Moiety ; Trifluoromethyl ; Chemistry ; Stereochemistry ; Ligand (biochemistry) ; Combinatorial chemistry ; Medicinal chemistry ; Organic chemistry ; Receptor ; Boron ; Alkyl ; Catalysis ; Biochemistry Ferrocene Chemistry and Applications ; Estrogen and related hormone effects ; Chemical Synthesis and Analysis
Authors: Florent HAFFNER | Marion Lacoue-Nègre | Aurélie Pirayre | D. Oliveira Goncalves | Julien Gornay | Maxime Moreaud
Source: Fuel (article, 2024-09-10)
Field: Chemistry — Analytical Chemistry

IPA: A deep CNN based on Inception for Petroleum Analysis Deep learning is emerging as a challenger to chemometrics which was used for decades to predict properties of petroleum cuts using Near-Infrared spectroscopy (NIR). In this study, a new CNN ”Inception for Petroleum Analysis” (IPA) has been developed and its performance compared with both PLS and recent deep learning reference model DeepSpectra to predict CN of middle distillates. IPA, is inspired from state-of the-art computer vision architectures. It is based on several computational blocks with the ambition to meet the daily needs in NIR analysis and is especially suited for small datasets like less than 250 samples. Two datasets were considered, a first one with a reduced spectral range, using variable selection, in which saturated variables have been removed for PLS modeling, and a larger dataset, without variable selection, to evaluate the robustness of IPA. IPA demonstrates regression errors 40% smaller than PLS, and therefore much closer to measured data. Compared with a benchmark Deep Learning model, in particular DeepSpectra, IPA is 20% more accurate. On the same dataset in its full-range of 4150 features, IPA demonstrates a regression error 50% smaller than PLS, and 21% more accurate than DeepSpectra. These results are due to the ability of IPA to both extract complex representation from spectra and not being distracted by parts of the dataset who are considered saturated by experts. This new method improves the performance and reduces the dependence on complex pre-processing operations on dataset, like variable selection and digital signal processing. • Development of a new deep learning architecture for NIR analysis. • The model has been applied to fuel data. • Model robustness improvement over PLS. Petroleum ; Petroleum engineering ; Computer science ; Artificial intelligence ; Environmental science ; Pattern recognition (psychology) ; Chemistry ; Geology ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Petroleum Processing and Analysis
Authors: Pavel Borozdin | E. Yu. Erushin | Artem Kozmin | Anastasia Bednyakova | И. Б. Мирошниченко | Nadezhda Y. Kostyukova | Andrey Boyko | Alexey Redyuk
Source: Sensors (article, 2024-11-25)
Field: Chemistry — Spectroscopy

Temperature-Based Long-Term Stabilization of Photoacoustic Gas Sensors Using Machine Learning In this study, we address the challenge of estimating the resonance frequency of a photoacoustic detector (PAD) gas cell under varying temperature conditions, which is crucial for improving the accuracy of gas concentration measurements. We introduce a novel approach that uses a long short-term memory network and a self-attention mechanism to model resonance frequency shifts based on temperature data. To investigate the impact of the gas mixture temperature on the resonance frequency, we modified the PAD to include an internal temperature sensor. Our experiments involved multiple heating and cooling cycles with varying methane concentrations, resulting in a comprehensive dataset of temperature and resonance frequency measurements. The proposed models were trained and validated on this dataset, and the results demonstrate real-time prediction capabilities with a mean absolute error of less than 1 Hz for frequency shifts exceeding 30 Hz over four-hour periods. This approach allows continuous, real-time tracking of the resonance frequency without interrupting the laser operation, significantly enhancing gas concentration measurements and contributing to the long-term stabilization of the sensor. The results suggest that the proposed approach is effective in managing temperature-induced frequency shifts, making it a valuable tool for improving the accuracy and stability of gas sensors in practical applications. Resonance (particle physics) ; Term (time) ; Tracking (education) ; Detector ; Stability (learning theory) ; Temperature measurement ; Methane ; Photoacoustic imaging in biomedicine ; Computer science ; Acoustics ; Materials science ; Chemistry ; Optics ; Physics ; Atomic physics ; Thermodynamics ; Machine learning ; Telecommunications Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Advanced Sensor Technologies Research
Authors: Shuning Wang | Yan‐Lin He | Hangwei Zhu | Haoxuan Wang
Source: Photonics (article, 2024-01-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An Efficient Design Method for a Metasurface Polarizer with High Transmittance and Extinction Ratio In fields such as polarization navigation and imaging, there is a demand for polarizers to simultaneously possess a high transmittance and extinction ratio. However, most studies focus solely on a single metric for polarizer design. To address this issue, this paper proposed a metasurface polarizer with a transition layer and its efficient design method, aiming to enhance the polarizers’ transmittance and extinction ratio performance simultaneously. An all-dielectric metasurface polarizer with a TiO2 transition layer was designed, and a tandem neural network was constructed by combining forward prediction and inverse design networks. The network is successfully trained by utilizing structural parameters and spectral response Tx and Ty datasets collected through simulation software. A high-performance metasurface polarizer was designed under the desired metrics with a transmittance of 90% and an extinction ratio of 40 dB. The proposed method in this paper made progress in the comprehensive optimization of polarizer performance. It is more accurate and efficient compared to other design methods and is better suited for practical applications. Polarizer ; Extinction ratio ; Transmittance ; Optics ; Polarization (electrochemistry) ; Computer science ; Materials science ; Optoelectronics ; Birefringence ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Animal Vocal Communication and Behavior
Authors: Zihan Chen | Haoyun Wang | Jufei Wang | Huanliang Xu | Ni Mei | Sixu Zhang
Source: Agriculture (article, 2024-08-25)
Field: Chemistry — Analytical Chemistry

Non-Destructive Detection Method of Apple Watercore: Optimization Using Optical Property Parameter Inversion and MobileNetV3 Current methods for detecting apple watercore are expensive and potentially damaging to the fruit. To determine whether different batches of apples are suitable for long-term storage or long-distance transportation, and to classify the apples according to quality level to enhance the economic benefits of the apple industry, it is essential to conduct non-destructive testing for watercore. This study proposes an innovative detection method based on optical parameter inversion and the MobileNetV3 model. Initially, a three-layer plate model of apples was constructed using the Monte Carlo method to simulate the movement of photons inside the apple, generating a simulated brightness map of photons on the apple’s surface. This map was then used to train the MobileNetV3 network with dilated convolution, resulting in a pre-trained model. Through transfer learning, this model was applied to measured spectral data to detect the presence of watercore. Comparative experiments were conducted to determine the optimal transfer strategy for the frozen layers, achieving model accuracy rates of 99.13%, 97.60%, and 95.32% for two, three, and four classifications, respectively. Furthermore, the model parameters were low at 7.52 M. Test results of this study confirmed the effectiveness and lightweight characteristics of the method that combines optical property parameter inversion, the DC-MobileNetV3 model, and transfer learning for detecting apple watercore. This model provides technical support to detect watercore and other internal diseases in apples. Inversion (geology) ; Biological system ; Property (philosophy) ; Materials science ; Botany ; Environmental science ; Mineralogy ; Computer science ; Remote sensing ; Horticulture ; Mathematics ; Biology ; Chemistry ; Geology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: Jérôme Deumer | Elina Andresen | Christian Gollwitzer | Robin Schürmann | Ute Resch‐Genger
Source: Analytical Chemistry (article, 2024-11-13)
Field: Materials Science — Materials Chemistry

Adding More Shape to Nanoscale Reference Materials─LiYF<sub>4</sub>:Yb,Tm Bipyramids as Standards for Sizing Methods and Particle Number Concentration The increasing industrial use of nanomaterials calls for the reliable characterization of their physicochemical key properties like size, size distribution, shape, and surface chemistry, and test and reference materials (RMs) with sizes and shapes, closely matching real-world nonspheric nano-objects. An efficient strategy to minimize efforts in producing nanoscale RMs (nanoRMs) for establishing, validating, and standardizing methods for characterizing nanomaterials are multimethod nanoRMs. Ideal candidates are lanthanide-based, multicolor luminescent, and chemically inert nanoparticles (NPs) like upconversion nanoparticles (UCNPs), which can be prepared in different sizes, shapes, and chemical composition with various surface coatings. This makes UCNPs interesting candidates as standards not only for sizing methods, but also for element-analytical methods like laser ablation-inductively coupled plasma mass spectrometry (LA-ICP-MS), quantitative bioimaging methods like X-ray fluorescence computed tomography (XFCT), and luminescence methods and correlative measurements. Here, we explore the potential of two monodisperse LiYF<sub>4</sub>:Yb,Tm bipyramids with peak-to-peak distances of (43 ± 2) nm and (29 ± 2) nm as size standards for small-angle X-ray scattering (SAXS) and tools for establishing and validating the sophisticated simulations required for the analysis of SAXS data derived from dispersions of nonspheric nano-objects. These SAXS studies are supplemented by two-dimensional (2D)-transmission electron microscopy measurements of the UCNP bipyramids. Additionally, the particle number concentration of cyclohexane dispersions of these UCNP bipyramids is determined by absolute SAXS measurements, complemented by gravimetry, thermogravimetric analysis (TGA), and inductively coupled plasma optical emission spectrometry (ICP-OES). This approach enables traceable particle number concentration measurements of ligand-capped nonspheric particles with unknown chemical composition. Chemistry ; Sizing ; Nanoscopic scale ; Particle (ecology) ; Nanotechnology ; Materials science Luminescence Properties of Advanced Materials ; Gas Sensing Nanomaterials and Sensors ; Atomic and Subatomic Physics Research
Authors: Guanhua Zhang | Han Zheng | Xiao Wang | Shuxin Han | Wei Liu | Chenglong Sun | Qiongzheng Hu | Chunxia Ma
Source: Plant Biotechnology Journal (article, 2024-10-04)
Field: Chemistry — Spectroscopy

Flexible substrate‐based mass spectrometry platform for <i>in situ</i> non‐destructive molecular imaging of living plants Monitoring and localizing molecules on living plants is critical for understanding their growth, development and disease. However, current techniques for molecular imaging of living plants often lack spatial information or require tedious pre-labelling. Here, we proposed a novel molecular imaging platform that combines sliver nanowire-doped Ti<sub>3</sub>C<sub>2</sub> MXene (Ag NWs@MXene) flexible film substrate with laser desorption/ionization mass spectrometry imaging (AMF-LDI-MSI) to study the spatial distribution of biomolecules on the surface of living plants. This platform overcomes the MSI challenges posed by difficult-to-slice plant tissues (e.g., tough or water-rich roots and fragile flowers) and enables precisely transfer and visualize the molecule. Comparisons of the measurement results to those from matrix-assisted LDI-MSI (MALDI-MSI) technology demonstrate the accuracy and reliability of the platform. Biocompatibility evaluations indicated that the platform without observable adverse effects on the health of living plants. The distribution of growth and disease-associated signalling molecules, such as choline, organic acids and carbohydrates, can be in situ non-destructively detected on the surfaces of living plants, which is important for tracking the health of plants and their diseased areas. AMF-LDI-MSI platform can serve as a promising tool for label-free, in situ and non-destructive monitoring of functional biomolecules and plant growth from a spatial perspective. Mass spectrometry imaging ; Biomolecule ; In situ ; Nanotechnology ; Substrate (aquarium) ; Molecular imaging ; Biology ; Mass spectrometry ; Materials science ; Computational biology ; Chemistry ; Biotechnology ; Ecology ; In vivo ; Chromatography Mass Spectrometry Techniques and Applications ; Luminescence and Fluorescent Materials ; MXene and MAX Phase Materials