jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Benjamin A. Neely | Yasset Pérez‐Riverol | Magnus Palmblad
Source: ChemRxiv (preprint, 2024-04-25)
Field: Chemistry — Spectroscopy

Quality Control in the Mass Spectrometry Proteomics Core: a Practical Primer The past decade has seen widespread advances in quality control (QC) materials and software tools focused specifically on mass spectrometry-based proteomics, yet the rate of adoption is inconsistent. Despite the fundamental importance of QC, it typically falls behind learning new techniques, instruments, or software. Considering how important QC is in a core setting where data is generated for non-mass spectrometry experts and confidence in delivered results is paramount, we have created this quick-start guide focusing on off-the-shelf QC materials and relatively easy to use QC software. We hope that by providing a background on the different levels of QC, different materials and their uses, describing QC design options, and highlighting some current QC software, that implementing QC in a core setting will be easier than ever. There continues to be development in each of these areas (such as new materials and software), and the current generation of QC mass spectrometry-based proteomics is more than capable of conveying confidence in results as well as minimizing laboratory downtime by guiding experimental, technical, and analytical troubleshooting from sample to results. Troubleshooting ; Computer science ; Software ; Downtime Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Sheng Jiang | Fangnan Xie | Jiangbo Ao | Yechen Wei | Jingye Lu | Shilei Lyu | Zhen Li
Source: Agronomy (article, 2024-10-31)
Field: Chemistry — Analytical Chemistry

Segmentation-Based Detection for Luffa Seedling Grading Using the Seg-FL Model This study addresses the issue of inaccurate and error-prone grading judgments in luffa plug seedlings. A new Seg-FL seedling segmentation model is proposed as an extension of the YOLOv5s-Seg model. The small leaves of early-stage luffa seedlings are liable to be mistaken for impurities in the plug trays. To address this issue, cross-scale connections and weighted feature fusion are introduced in order to integrate feature information from different levels, thereby improving the recognition and segmentation accuracy of seedlings or details by refining the PANet structure. To address the ambiguity of seedling edge information during segmentation, an efficient channel attention module is incorporated to enhance the network’s focus on seedling edge information and suppress irrelevant features, thus sharpening the model’s focus on luffa seedlings. By optimizing the CIoU loss function, the calculation of overlapping areas, center point distances, and aspect ratios between predicted and ground truth boxes is preserved, thereby accelerating the convergence process and reducing the computational resource requirements on edge devices. The experimental results demonstrate that the proposed model attains a mean average precision of 97.03% on a self-compiled luffa plug seedling dataset, representing a 6.23 percentage point improvement over the original YOLOv5s-Seg. Furthermore, compared to the YOLACT++, FCN, and Mask R-CNN segmentation models, the improved model displays increases in mAP@0.5 of 12.93%, 13.73%, and 10.53%, respectively, and improvements in precision of 15.73%, 16.93%, and 13.33%, respectively. This research not only validates the viability of the enhanced model for luffa seedling grading but also provides tangible technical support for the automation of grading in agricultural production. Seedling ; Grading (engineering) ; Segmentation ; Artificial intelligence ; Computer science ; Horticulture ; Biology ; Ecology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: Shuyan Hao | Ting Xia | Ruizhi Zhang | Meng Guo
Source: Scientific Reports (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

Clustering Cu-S based compounds using periodic table representation and compositional Wasserstein distance Crystal structure similarity is useful for the chemical analysis of nowadays big materials databases and data mining new materials. Here we propose to use two-dimensional Wasserstein distance (earth mover's distance) to measure the compositional similarity between different compounds, based on the periodic table representation of compositions. To demonstrate the effectiveness of our approach, 1586 Cu-S based compounds are taken from the inorganic crystal structure database (ICSD) to form a validation dataset. By using local structure order parameters as a geometrical similarity metric, the similarity matrix including both compositional and geometrical similarities is calculated. Then all the Cu-S compounds are clustered into 86 groups using the similarity matrix and "density-based spatial clustering of applications with noise" (DBSCAN) algorithm. Some selected groups are analyzed using crystal structure visualization of hundreds of compounds, which provides chemical insights of the similarity metrics and shows the effectiveness of clustering. A group of rare earth containing layered Cu-S compounds is proposed for further experimental investigation as potential thermoelectric materials, based on a structure-property relationship consideration that similar structures tend to have similar properties. The unsupervised clustering approach in this work can be easily applied to other datasets, which will help for chemical understanding of the materials datasets and discover new materials with similarity properties based on the similarity metrics. Cluster analysis ; Table (database) ; Representation (politics) ; Computer science ; Mathematics ; Data mining ; Artificial intelligence Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Computational Drug Discovery Methods
Authors: Wenqi Lou | Luiz F. Brito | Xiuxin Zhao | V. Bonfatti | Jianbin Li | Yachun Wang
Source: Animal Research and One Health (article, 2024-07-08)
Field: Chemistry — Analytical Chemistry

Selection of the most informative wavenumbers to improve prediction accuracy of milk fatty acid profile based on milk mid‐infrared spectra data Abstract Milk mid‐infrared (MIR) spectra have been shown to provide valuable information on a wide range of traits to be used in dairy cattle breeding programs. Selecting the most informative variables from complex data can improve the prediction accuracy and model robustness and, consequently, the interpretability of MIR spectra. Thus, we aimed to investigate the prediction performance of feature selection methods based on MIR spectra data, using the milk fatty acid (FA) profile as an example to illustrate the evaluated procedure. Data of MIR spectra, milk test‐day records, and reference FA concentrations of 155 first‐parity Holstein cows were used in the analyses. Four models comprising different explanatory variables and three feature selection methods were evaluated. The results indicated that competitive adaptive reweighted sampling (CARS) method can effectively select the most informative variables from the MIR spectra, resulting in higher prediction accuracies than other variable selection approaches. The model including selected MIR spectra and cow information variables yielded the best FA profile predictions based on partial least square regression. C8:0, C10:0, C14:1, C17:0 isomers, C18:1, C18:1 isomer, medium‐chain FA, unsaturation FA, monounsaturated FA, and polyunsaturated FA presented accuracies based on the determination coefficient ranging from 0.66 to 0.85 in internal validation and from 0.65 to 0.84 in external validation. The most related wavenumbers to 35 FAs were found within 1003 to 1145 cm −1 . Generally, using CARS and cow information improved predictions of FAs based on MIR spectra in Chinese Holstein dairy cows. Additional validation studies should be conducted as larger datasets become available. Wavenumber ; Fatty acid ; Selection (genetic algorithm) ; Food science ; Chemistry ; Chromatography ; Computer science ; Artificial intelligence ; Biochemistry ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Zibin Huang | Wenhai Wang | Yuyue Yang | Hanzhe Zhang | Yuhang Wang | T. C. Zhao | Yuan Zhi Pan | Huiru Sun | Xuanhui Qu | Jiang Zeng | Xiaojie Su | Wenzhe Dong | Lin Hu | Hongyu Chen | Shichen Su
Source: Nano Research (article, 2024-06-01)
Field: Materials Science — Materials Chemistry

Controllable and reversible photoresponses in graphene-gated WSe <sub>2</sub> field-effect transistors for communication, logic gate operations and image pre-processing A tunable, controllable, and reversible photoresponse within a single device holds significant potential for broad-spectrum image detection, convolutional computation, and autonomous systems. This study presents the successful fabrication of a graphene-gated WSe<sub>2</sub> heterojunction field-effect transistor (FET), utilizing graphene as a highly transparent gate electrode. By modulating the graphene-gated voltage, which affects the effective bias voltage, the current is redirected into both the graphene and metal electrodes along the WSe<sub>2</sub>. The device exhibits notable and adjustable positive photoconductance (V<sub>g</sub> &lt; 3.25 V) and negative photoconductance (V<sub>g</sub> &gt; 3.25 V) under 520 nm illumination. The responsivity of positive photoconductance (V<sub>g</sub> = -5 V) and negative photoconductance (V<sub>g</sub> = 5 V) values are 427 mA/W and 425 mA/W, respectively. Additionally, the device demonstrates excellent performance, with a high fitting exponent (0.99) between power intensity and photocurrent at zero bias due to the photovoltaic effect. Leveraging its controllable and reversible photoresponse, along with the photovoltaic effect, the graphene-gated WSe<sub>2</sub> heterojunction FET enables multifunctional applications, including encrypted communication, “OR” logic operations, three-state logic gates and imaging pre-processing. Besides, images can be pre-processed and enhanced by integrating convolutional neural networks. These findings highlight the promising potential of high-performance multifunctional optoelectronic devices. Graphene ; Field-effect transistor ; Logic gate ; Optoelectronics ; Transistor ; Nanotechnology ; Materials science ; Computer science ; Electronic engineering ; Electrical engineering ; Engineering ; Voltage 2D Materials and Applications ; Advanced Memory and Neural Computing ; Graphene research and applications
Authors: Panagiotis Koutsogiannis | P. A. Algarabel | J. A. Pardo | César Magén
Source: APL Materials (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Engineering polar distortions in multiferroic Sr1−xBaxMnO3−δ thin films The physical properties of perovskite oxide thin films are governed by the subtle interplay between chemical composition and crystal symmetry variations, which can be altered by epitaxial growth. In the case of perovskite-type multiferroic thin films, precise control of stoichiometry and epitaxial strain allows for gaining control over the ferroic properties through selective crystal distortions. Here, we demonstrate the chemical tailoring of the polar atomic displacements by tuning the stoichiometry of multiferroic Sr1−xBaxMnO3−δ (0 ≤ x ≤ 0.5) epitaxial thin films. A combination of x-ray diffraction and aberration-corrected scanning transmission electron microscopy enables unraveling the local polarization orientation at the nanoscale as a function of the film’s composition and induced crystalline structure. We demonstrate experimentally that the orientation of polarization is intimately linked to the Ba doping and O stoichiometry of the films and, with the biaxial strain induced by the substrate, it can be tuned either in-plane or out-of-plane with respect to the substrate by the appropriate choice of the post-growth annealing temperature and O2 atmosphere. This chemistry-mediated engineering of the polarization orientation of oxide thin films opens new venues for the design of functional multiferroic architectures and the exploration of novel physics and applications of ferroelectric textures with exotic topological properties. Multiferroics ; Materials science ; Ferroelectricity ; Thin film ; Piezoresponse force microscopy ; Epitaxy ; Polarization (electrochemistry) ; Strain engineering ; Perovskite (structure) ; Condensed matter physics ; Scanning transmission electron microscopy ; Stoichiometry ; Nanotechnology ; Crystallography ; Optoelectronics ; Transmission electron microscopy ; Dielectric ; Silicon ; Chemistry ; Physical chemistry ; Physics Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Magnetic and transport properties of perovskites and related materials
Authors: Eleonora Iaccheri | Annachiara Berardinelli | G. Ceredi | Luigi Ragni
Source: Computers and Electronics in Agriculture (article, 2024-12-02)
Field: Chemistry — Analytical Chemistry

Non-destructive assessment of the internal defects of FRED® pear by a low radio-frequency capacitive technique • Capacitive device for pear post-harvest management. • Affordable, rapid, and non-destructive internal pear browning assessment. • Classification with fuzzy C-means clustering analysis. Postharvest pear fruit internal browning damages were assessed non-destructively using a capacitive instrumental chain in the super low (SLF) – low (LF) radiofrequency region combined with Image analysis. A rapid, inexpensive, and non-destructive instrumental chain was set up: an LCR meter interfaced with a PC receives and transmits signals through a parallel plate capacitor in the frequency range from 100 Hz to 10 kHz. The capacitance data were used to classify the FRED® pear fruit into two classes, “with defects” and “healthy”, using the unsupervised fuzzy C-means clustering analysis. The main results show good classification rates: 91 % of the samples are correctly classified as damaged. Further samples should be implemented to understand how the model performs with samples from different ripening stages. Despite this, the technique appears promising for non-destructive internal quality assessment in pear fruit. Capacitive sensing ; PEAR ; Engineering ; Radio frequency ; Acoustics ; Electrical engineering ; Computer science ; Forensic engineering ; Physics ; World Wide Web Spectroscopy and Chemometric Analyses ; Material Properties and Processing ; Industrial Vision Systems and Defect Detection
Authors: Johannes Popp | Lukas Seitner | Felix Naunheimer | Georg Janowski | Michael Haider | Christian Jirauschek
Source: IEEE photonics journal (article, 2024-02-27)
Field: Chemistry — Spectroscopy

Multi-Domain Modeling of Free-Running Harmonic Frequency Comb Formation in Terahertz Quantum Cascade Lasers Optical frequency comb (OFC) emission in quantum cascade lasers (QCLs) is highly attractive for applications in metrology and sensing. Recently, coherent OFC mode-locking with large intermodal spacing was demonstrated in QCLs. These self-starting harmonic frequency combs (HFCs) show highly phase-stable operation and promise interesting perspectives towards optical or even quantum communication. Here, we present a detailed multi-domain modeling approach for the numerical study of QCLs. Our theoretical characterization is divided into stationary carrier transport simulations, based on the ensemble Monte Carlo method, and dynamical simulations of the light-matter interaction, based on multi-level Maxwell-density matrix equations. We investigate the influence of the chosen eigenstate basis on the gain spectrum and present self-consistent simulation results of stable HFC operation in a double metal terahertz QCL. In our simulations, the studied QCL gain medium shows self-starting harmonic mode-locking for different bias and waveguide configurations, resulting in a mode spacing of up to twelve times the cavity round trip frequency. Furthermore, we characterize the spectral time evolution of the coherent HFC formation process, yielding the spontaneous build-up of a dense multimode state which is gradually transferred into a broad and clear harmonic OFC state. Cascade ; Terahertz radiation ; Physics ; Laser ; Quantum cascade laser ; Frequency domain ; Harmonic ; Semiconductor laser theory ; Frequency comb ; Quantum ; Monte Carlo method ; Optoelectronics ; Optics ; Quantum mechanics ; Computer science Spectroscopy and Laser Applications ; Advanced Fiber Laser Technologies ; Photonic and Optical Devices
Authors: Borjan Geshkovski | Hugo Koubbi | Yury Polyanskiy | Philippe Rigollet
Source: arXiv (Cornell University) (preprint, 2024-10-09)
Field: Materials Science — Materials Chemistry

Dynamic metastability in the self-attention model We consider the self-attention model - an interacting particle system on the unit sphere, which serves as a toy model for Transformers, the deep neural network architecture behind the recent successes of large language models. We prove the appearance of dynamic metastability conjectured in [GLPR23] - although particles collapse to a single cluster in infinite time, they remain trapped near a configuration of several clusters for an exponentially long period of time. By leveraging a gradient flow interpretation of the system, we also connect our result to an overarching framework of slow motion of gradient flows proposed by Otto and Reznikoff [OR07] in the context of coarsening and the Allen-Cahn equation. We finally probe the dynamics beyond the exponentially long period of metastability, and illustrate that, under an appropriate time-rescaling, the energy reaches its global maximum in finite time and has a staircase profile, with trajectories manifesting saddle-to-saddle-like behavior, reminiscent of recent works in the analysis of training dynamics via gradient descent for two-layer neural networks. Balanced flow ; Metastability ; Saddle ; Context (archaeology) ; Statistical physics ; Computer science ; Physics ; Mathematics ; Mathematical analysis ; Quantum mechanics ; Mathematical optimization ; Geology Block Copolymer Self-Assembly ; Theoretical and Computational Physics ; Quantum many-body systems
Authors: Al-Obaidi Sudad H
Source: Petroleum & Petrochemical Engineering Journal (article, 2024-01-23)
Field: Chemistry — Analytical Chemistry

An Application of Polymer Nanocomposites Based On Carboxymethylcellulose along with Aluminium (Al) and Copper (Cu) Nanoparticles for Increasing Oil Production As an agent for displacing residual and hard-to-recover oil reserves, polymer nanocomposites based on sodium salts of carboxymethylcellulose (CMC) and aluminium (Al) and copper (Cu) nanoparticles were investigated. It has been shown that polymer nanocomposites are more effective as oil displacement agents than the sodium salt of carboxymethylcellulose itself. Increasing the viscosity of an oil displacement solution can be achieved by varying the concentrations of CMC and nanoparticles. This study thoroughly examined the dynamic viscosities of carboxymethylcellulose (CMC) and polymer nanocomposites that contain CMC and metal nanoparticles (Al and Cu) across various concentrations. The results obtained are robust and reliable. It has been shown that, among polymer nanocomposites containing Al nanoparticles, there is a higher dynamic viscosity compared to its Cu nanoparticle counterpart at equivalent concentrations. This work proposes a simulation reservoir model to enhance oil recovery from hard-to-recovery reserves. Through this reservoir model simulation, the oil recovery factor was calculated using filler sand and oil extracted from a hydrocarbon field. The experimental data revealed the most effective concentration of CMC and nanoparticles for oil displacement. The results suggest that polymer nano composites composed of different metal nano powders have varying impacts on oil displacement efficiency. In this experiment, a composition consisting of Al nanoparticles with a size range of 50-70 manometers (nm) proved to be more successful as an oil displacement agent than a polymer nanocomposite made of Cu nanoparticles and CMC. At these concentrations and combinations, the highest oil recovery factors were achieved. Aluminium ; Copper ; Nanocomposite ; Nanoparticle ; Materials science ; Polymer ; Chemical engineering ; Metallurgy ; Nanotechnology ; Composite material ; Engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Venkatesh Mishra | Satyendra Pratap Singh | Sumit Kaur Bhatia | Vishal Singh Chandel | Rajiv Manohar
Source: Research in Agricultural Engineering (article, 2024-09-09)
Field: Chemistry — Analytical Chemistry

Detecting adulteration in mustard oil using low-frequency dielectric spectroscopy This paper presents adielectric spectroscopy approach for analysing the quality offood products. This study aimed todetect the adulteration inmustard oil using dielectric spectroscopy inthe 1 to10MHz frequency range ata temperature of30 to50C. The dielectric data were used topredict the adulteration inoils atthe given frequency range. The finding indicates that using data analysis techniques can further improve the capacity ofdielectric sensing todetect adulterated edible oil. Using MATLAB R2021a, linear relationships between the frequency and adulteration percentage variables were obtained topredict the dielectric constant and dielectric loss factor values. Apaired sample t-test was used toanalyse the effects ofthe frequency and adulteration onthe dielectric parameters, with asignificance level of0.05 being set for the differences. Correlation coefficients (R2) > 0.96 were established using regression equations relating the dielectric constant, dielectric loss, and adulteration. Dielectric spectroscopy ; Spectroscopy ; Materials science ; Acoustics ; Environmental science ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis
Authors: Lucien Laiarinandrasana | Olga Klinkova | Cristian Ovalle | Peter Cloetens | Henry Proudhon | Thilo F. Morgeneyer
Source: Journal of Theoretical Computational and Applied Mechanics (article, 2024-09-27)
Field: Materials Science — Polymers and Plastics

Deformation and cavitation at the spherulite scale of an isotactic polypropylene Engineering stress-strain curves are generated from tensile tests on semi-crystalline thermoplastics, which may exhibit non-linearity and/or peak stress associated with striction/necking phenomenon of the specimen at the macroscopic scale. This work addresses this state of deformed specimen, on an isotactic polypropylene, where irreversible strains have led to a variable cross-sectional area along the necked region. 3D images in this region, obtained through Synchrotron Radiation Computed Tomography with two high resolutions are exploited. The best resolution (1 pixel length = 0.05 μm) allowed better understanding of the morphology of several deformed spherulites within which polar fan arrangements are clearly detailed. Thanks to the dentification of the boundaries of spherulite patterns, with a 0.7 μm resolution, the longitudinal and transverse elongations of larger numbers of spherulites are measured. The evolution of the volumetric plastic strains due to cavitation at the spherulitic scale along the necked regions is comprehensively analysed. Volume changes at this scale are highlighted, consisting of an increase in the case of void growth followed by a decrease at large strains due to the collapse of elongated voids. The effects of these results on the establishment of reliable constitutive model are discussed. It is found that accounting for plastic dilation is necessary for the accuracy of constitutive models. Tacticity ; Spherulite (polymer physics) ; Cavitation ; Materials science ; Polypropylene ; Deformation (meteorology) ; Composite material ; Polymer science ; Mechanics ; Physics ; Polymer ; Polymerization Polymer crystallization and properties ; Polymer Nanocomposites and Properties