jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hang Hu | David Helminiak | Manxi Yang | Daisy Unsihuay | Ryan T. Hilger | Dong Hye Ye | Julia Laskin
Source: ACS Measurement Science Au (article, 2022-08-15)
Field: Chemistry — Spectroscopy

High-Throughput Mass Spectrometry Imaging with Dynamic Sparse Sampling Mass spectrometry imaging (MSI) enables label-free mapping of hundreds of molecules in biological samples with high sensitivity and unprecedented specificity. Conventional MSI experiments are relatively slow, limiting their utility for applications requiring rapid data acquisition, such as intraoperative tissue analysis or 3D imaging. Recent advances in MSI technology focus on improving the spatial resolution and molecular coverage, further increasing the acquisition time. Herein, a deep learning approach for dynamic sampling (DLADS) was employed to reduce the number of required measurements, thereby improving the throughput of MSI experiments in comparison with conventional methods. DLADS trains a deep learning model to dynamically predict molecularly informative tissue locations for active mass spectra sampling and reconstructs high-fidelity molecular images using only the sparsely sampled information. Experimental hardware and software integration of DLADS with nanospray desorption electrospray ionization (nano-DESI) MSI is reported for the first time, which demonstrates a 2.3-fold improvement in throughput for a linewise acquisition mode. Meanwhile, simulations indicate that a 5-10-fold throughput improvement may be achieved using the pointwise acquisition mode. Mass spectrometry imaging ; Throughput ; Mass spectrometry ; Sampling (signal processing) ; Computer science ; Chemistry ; Chromatography ; Computer vision ; Telecommunications Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Tran Doan Huan | Aubrey Toland | Kellie A. Stellmach | McKinley K. Paul | Will R. Gutekunst | Rampi Ramprasad
Source: The Journal of Physical Chemistry Letters (article, 2022-05-25)
Field: Materials Science — Materials Chemistry

Toward Recyclable Polymers: Ring-Opening Polymerization Enthalpy from First-Principles Ring-opening polymerization (ROP) enthalpy Δ<i>H</i><sup>ROP</sup> is an important thermodynamic property controlling the polymerization of cyclic monomers. While Δ<i>H</i><sup>ROP</sup> can be measured, computing Δ<i>H</i><sup>ROP</sup> for realistic polymer systems with an error of ≃5-10 kJ/mol is critical for designing new monomer systems for depolymerizable polymers. We have developed a first-principles computational scheme in which multiple challenges in computing Δ<i>H</i><sup>ROP</sup> are resolved definitively including extensive exploration of conformational states and adequately addressing finite size effects. This scheme is validated on a diverse benchmark set of 42 ROP polymers for which reliable experimental values of Δ<i>H</i><sup>ROP</sup> are available. For this set, the Δ<i>H</i><sup>ROP</sup> root-mean-square error is ≃7 kJ/mol, about 3-times smaller than conventional approaches. This development opens up new pathways to build up a high-quality database of Δ<i>H</i><sup>ROP</sup> for downstream predictive machine-learning models and ultimately to accelerate the design of depolymerizable polymers with desired properties. Polymerization ; Monomer ; Polymer ; Benchmark (surveying) ; Enthalpy ; Computer science ; Set (abstract data type) ; Ring (chemistry) ; Materials science ; Thermodynamics ; Chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis ; Polymer crystallization and properties
Authors: Jiajun Meng | Luke Weston | Sivacarendran Balendhran | Dandan Wen | Jasper J. Cadusch | Ranjith Rajasekharan Unnithan | Kenneth B. Crozier
Source: Laser & Photonics Review (article, 2022-01-24)
Field: Chemistry — Organic Chemistry

Compact Chemical Identifier Based on Plasmonic Metasurface Integrated with Microbolometer Array Abstract The identification of chemicals from their mid‐infrared spectra has applications that include industrial production of chemicals, food production, pharmaceutical manufacturing, and environmental monitoring. This is generally done using laboratory benchtop tools, such as the Fourier transform infrared spectrometer. Although such systems offer high performance, alternative platforms offering reduced size, weight, and cost can enable a host of new applications, e.g. in consumer personal electronics. Here a compact microspectrometer platform for chemical identification, comprising a mid‐infrared metasurface integrated with a lightweight (≈1 g) and very small (≈1 cm 3 ) microbolometer‐based thermal camera is experimentally demonstrated. A machine learning algorithm is trained to analyze the microspectrometer output and classify chemicals based on their mid‐infrared fingerprints. High accuracy identification of four liquid chemicals, concentration quantification of ethyl lactate in cyclohexane down to subpercentage levels, and the classification of food and drug samples is demonstrated. Microbolometer ; Identification (biology) ; Spectrometer ; Computer science ; Infrared ; Materials science ; Fourier transform infrared spectroscopy ; Embedded system ; Nanotechnology ; Optics ; Telecommunications ; Physics ; Bolometer Polydiacetylene-based materials and applications ; Advanced Chemical Sensor Technologies ; Gas Sensing Nanomaterials and Sensors
Authors: Jianbo Hu | Jiyu Cui | Bin Gao | Lifeng Yang | Qi Ding | Yijian Li | Yiming Mo | Huajun Chen | Xili Cui | Huabin Xing
Source: Matter (article, 2022-08-22)
Field: Chemistry — Inorganic Chemistry

Machine-learning-assisted exploration of anion-pillared metal organic frameworks for gas separation Gas separation ; Metal-organic framework ; Ion ; Metal ions in aqueous solution ; Materials science ; Computer science ; Process engineering ; Chemistry ; Engineering ; Adsorption ; Organic chemistry ; Membrane Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Jun‐Li Xu | Puneet Mishra
Source: Analytica Chimica Acta (article, 2022-03-01)
Field: Chemistry — Analytical Chemistry

Combining deep learning with chemometrics when it is really needed: A case of real time object detection and spectral model application for spectral image processing Deep learning (DL) being popularly used in computer vision applications is still in its early stage in chemometric domain for spectral image processing. Often the challenge is that there are too few samples from analytical laboratory experiments to preform DL. In this study, we present a novel combination of DL and chemometrics to process spectral images even with as few as < 100 spectral images. We divided the image processing part such as object detection and recognition as the DL task and prediction of chemical property as the chemometric task based on latent space modelling. For image processing tasks of object detection and recognition, transfer learning was performed on the pretrained YOLOv4 object detection network weights to adapt the model to work well on spectral images captured in laboratory settings. Once the object is identified with DL, a background query is performed for the pre-built chemometric models to select the model for predicting the properties for specific object. The obtained results showed good potential of using DL and chemometric approaches in conjunction to reap the best of both scientific domains. This approach is of high interest to whoever involved in spectral imaging and dealing with object detection and physicochemical properties prediction of the samples with chemometric approaches. Chemometrics ; Artificial intelligence ; Pattern recognition (psychology) ; Object (grammar) ; Hyperspectral imaging ; Process (computing) ; Object detection ; Cognitive neuroscience of visual object recognition ; Computer science ; Task (project management) ; Chemistry ; Machine learning Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Advanced Chemical Sensor Technologies
Authors: Eliana C.A. de Brito | Thiago França | Thalita Canassa | Simone Schneider Weber | Anamaria Mello Miranda Paniago | Cícero Cena
Source: Photodiagnosis and Photodynamic Therapy (article, 2022-05-19)
Field: Chemistry — Analytical Chemistry

Paracoccidioidomycosis screening diagnosis by FTIR spectroscopy and multivariate analysis Paracoccidioidomycosis ; Fourier transform infrared spectroscopy ; Gold standard (test) ; Paracoccidioides brasiliensis ; Medicine ; Incidence (geometry) ; Internal medicine ; Gastroenterology ; Immunology ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Plant Pathogens and Fungal Diseases ; Biosensors and Analytical Detection
Authors: Muhammad Nasir Amin | Izaz Ahmad | Mudassir Iqbal | Asim Abbas | Kaffayatullah Khan | Muhammad Iftikhar Faraz | Anas Abdulalim Alabdullah | Shahid Ullah
Source: Materials (article, 2022-06-29)
Field: Materials Science — Materials Chemistry

Computational AI Models for Investigating the Radiation Shielding Potential of High-Density Concrete Concrete is an economical and efficient material for attenuating radiation. The potential of concrete in attenuating radiation is attributed to its density, which in turn depends on the mix design of concrete. This paper presents the findings of a study conducted to evaluate the radiation attenuation with varying water-cement ratio (<i>w</i>/<i>c</i>), thickness, density, and compressive strength of concrete. Three different types of concrete, i.e., normal concrete, barite, and magnetite containing concrete, were prepared to investigate this study. The radiation attenuation was calculated by studying the dose absorbed by the concrete and the linear attenuation coefficient. Additionally, artificial neural network (ANN) and gene expression programming (GEP) models were developed for predicting the radiation shielding capacity of concrete. A correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE) were calculated as 0.999, 1.474 mGy, 2.154 mGy and 0.994, 5.07 mGy, 5.772 mGy for the training and validation sets of the ANN model, respectively. Similarly, for the GEP model, these values were recorded as 0.981, 13.17 mGy, and 20.20 mGy for the training set, whereas the validation data yielded R = 0.985, MAE = 12.2 mGy, and RMSE = 14.96 mGy. The statistical evaluation reflects that the developed models manifested close agreement between experimental and predicted results. In comparison, the ANN model surpassed the accuracy of the GEP models, yielding the highest R and the lowest MAE and RMSE. The parametric and sensitivity analysis revealed the thickness and density of concrete as the most influential parameters in contributing towards radiation shielding. The mathematical equation derived from the GEP models signifies its importance such that the equation can be easily used for future prediction of radiation shielding of high-density concrete. Mean squared error ; Attenuation ; Correlation coefficient ; Parametric statistics ; Coefficient of determination ; Gene expression programming ; Attenuation coefficient ; Compressive strength ; Radiation ; Materials science ; Electromagnetic shielding ; Mathematics ; Statistics ; Composite material ; Computer science ; Physics ; Optics ; Machine learning Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies ; Geophysical Methods and Applications
Authors: Pengcheng Xu | Can Chen | Shuizhou Chen | Wencong Lu | Quan Qian | Yi Zeng
Source: ACS Omega (article, 2022-06-09)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Design of Yttria-Stabilized Zirconia Thermal Barrier Coatings with High Bonding Strength [Image: see text] As a high-quality thermal barrier coating material, yttria-stabilized zirconia (YSZ) can effectively reduce the temperature of the collective materials to be used on the surface of gas turbine hot-end components. The bonding strength between YSZ and the substrate is also one of the most important factors for the applications. Herein, the Gaussian mixture model (GMM) and support vector regression (SVR) were used to construct a machine learning model between YSZ coating bonding strength and atmospheric plasma spraying (APS) process parameters. First, GMM was used to expand the original 8 data points to 400 with the R value of leave-one-out cross-validation improved from 0.690 to 0.990. Then, the specific effects of APS process parameters were explored through Shapley additive explanations and sensitivity analysis. Principal component analysis was used to explain the constructed model and obtain the optimized area with a high bonding strength. After experimental validation, the results showed that under the APS process parameters of a current of 617 A, a voltage of 65 V, a H(2) flow of 3 L min(–1), and a thickness of 200 μm, the bonding strength increased by more than 19% to 55.5 MPa compared with the original maximum value of 46.6 MPa, indicating that the constructed GMM–SVR model can accurately predict the bonding strength of YSZ coating. Thermal barrier coating ; Yttria-stabilized zirconia ; Materials science ; Cubic zirconia ; Composite material ; Thermal ; Chemical engineering ; Ceramic ; Engineering Thermal properties of materials ; High-Temperature Coating Behaviors ; Thermal and Kinetic Analysis
Authors: Rui C. Martins | Teresa Barroso | P. A. S. Jorge | Mário Cunha | Filipe Neves dos Santos
Source: Computers and Electronics in Agriculture (article, 2022-01-29)
Field: Chemistry — Analytical Chemistry

Unscrambling spectral interference and matrix effects in Vitis vinifera Vis-NIR spectroscopy: Towards analytical grade ‘in vivo’ sugars and acids quantification Partial least squares regression ; Mathematics ; Matrix (chemical analysis) ; Covariance matrix ; Chemometrics ; Biological system ; Artificial intelligence ; Algorithm ; Chemistry ; Statistics ; Pattern recognition (psychology) ; Analytical Chemistry (journal) ; Computer science ; Machine learning ; Chromatography Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Sajedeh Safapoor | Mohammad G. Dekamin | Arezoo Akbari | Mohammad Reza Naimi‐Jamal
Source: Scientific Reports (article, 2022-06-24)
Field: Chemistry — Organic Chemistry

Synthesis of (E)-2-(1H-tetrazole-5-yl)-3-phenylacrylenenitrile derivatives catalyzed by new ZnO nanoparticles embedded in a thermally stable magnetic periodic mesoporous organosilica under green conditions ZnO nanoparticles embedded in a magnetic isocyanurate-based periodic mesoporous organosilica (Fe<sub>3</sub>O<sub>4</sub>@PMO-ICS-ZnO) were prepared through a modified environmentally-benign procedure for the first time and properly characterized by appropriate spectroscopic and analytical methods or techniques used for mesoporous materials. The new thermally stable Fe<sub>3</sub>O<sub>4</sub>@PMO-ICS-ZnO nanomaterial with proper active sites and surface area as well as uniform particle size was investigated for the synthesis of medicinally important tetrazole derivatives through cascade condensation and concerted 1,3-cycloaddition reactions as a representative of the Click Chemistry concept. The desired 5-substituted-1H-tetrazole derivatives were smoothly prepared in high to quantitative yields and good purity in EtOH under reflux conditions. Low catalyst loading, short reaction time and the use of green solvents such as EtOH and water instead of carcinogenic DMF as well as easy separation and recyclability of the catalyst for at least five consecutive runs without significant loss of its activity are notable advantages of this new protocol compared to other recent introduced procedures. Tetrazole ; Catalysis ; Mesoporous organosilica ; Mesoporous material ; Cycloaddition ; Materials science ; Nanoparticle ; Nanomaterials ; Chemical engineering ; Mesoporous silica ; Combinatorial chemistry ; Nanotechnology ; Organic chemistry ; Chemistry Synthesis of Tetrazole Derivatives ; Multicomponent Synthesis of Heterocycles ; Microwave-Assisted Synthesis and Applications
Authors: Eman Alhomaidi | Saade Abdalkareem Jasim | Hawraz Ibrahim M. Amin | Marcos A.L. Nobre | Mehrdad Khatami | Abduladheem Turki Jalil | Saja Hussain Dilfy
Source: IET Nanobiotechnology (article, 2022-08-30)
Field: Materials Science — Materials Chemistry

Biosynthesis of silver nanoparticles using <i>Lawsonia inermis</i> and their biomedical application Developing biosynthesis of silver nanoparticles (Ag-NPs) using plant extract is an environmentally friendly method to reduce the use of harmful chemical substances. The green synthesis of Ag-NPs by Lawsonia inermis extract and its cellular toxicity and the antimicrobial effect was studied. The physical and chemical properties of synthesised Ag-NPs were investigated using UV-visible spectroscopy, infrared spectroscopy, X-ray diffraction (XRD), scanning, and transmission electron microscopy. The average size of Ag-NPs was 40 nm. The XRD result shows peaks at 2θ = 38.07°, 44.26°, 64.43°, and 77.35° are related to the FCC structure of Ag-NPs. Cytotoxicity of synthesised nanoparticles was evaluated by MTT toxicity test on breast cancer MCF7 cell line. Observations showed that the effect of cytotoxicity of nanoparticles on the studied cell line depended on concentration and time. The obtained IC<sub>50</sub> was considered for cells at a dose of 250 μg/ml. Growth and survival rates decreased exponentially with the dose. Antimicrobial properties of Ag-NPs synthesised with extract were investigated against Escherichia coli, Salmonella typhimurium, Bacillus cereus, and Staphylococcus aureus to calculate the minimum inhibitory concentration and the minimum bactericidal concentration of (MBC). The results showed that the synthesised Ag-NPs and the plant extract have antimicrobial properties. The lowest concentration of Ag-NPs that can inhibit the growth of bacterial strains was 25 μg/ml. Lawsonia inermis ; Bacillus cereus ; Nuclear chemistry ; Antimicrobial ; Cytotoxicity ; Nanoparticle ; Silver nanoparticle ; Chemistry ; MTT assay ; Minimum inhibitory concentration ; Materials science ; Nanotechnology ; Bacteria ; Cell growth ; Biochemistry ; Biology ; Organic chemistry ; In vitro ; Traditional medicine Nanoparticles: synthesis and applications ; Moringa oleifera research and applications ; Medicinal Plant Research
Authors: Aseem Chawla | Adam J. Mallette | Rishabh Jain | Nam Q. Le | Francisco C. Robles Hernández | Jeffrey D. Rimer
Source: Microporous and Mesoporous Materials (article, 2022-06-06)
Field: Chemistry — Inorganic Chemistry

Crystallization of potassium-zeolites in organic-free media Zeolite ; Chabazite ; Crystallization ; Potassium ; Chemical engineering ; Chemistry ; Phase (matter) ; Alkali metal ; Crystal growth ; Materials science ; Catalysis ; Nanotechnology ; Crystallography ; Organic chemistry Zeolite Catalysis and Synthesis ; Chemical Synthesis and Characterization ; Mesoporous Materials and Catalysis
Authors: Hyunseok Song | Michael Abraham Listyawan | Jungho Ryu
Source: Actuators (article, 2022-12-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Core–Shell Magnetoelectric Nanoparticles: Materials, Synthesis, Magnetoelectricity, and Applications Nanoparticles with small diameters and large surface areas have potential advantages and are actively utilized in various fields related to biomedical and catalytic applications. Multifunctional applications can be achieved by endowing nanoparticles with piezoelectric, quantum dot, magnetothermal, and piezoluminescent properties. In particular, multiferroic magnetoelectric nanoparticles (MENPs) can generate electricity by coupling piezoelectric and magnetostrictive properties when an external magnetic field, which is harmless to the human body, is applied. In this regard, magnetoelectricity (ME) induced by a magnetic field makes MENPs useful for various biomedical and electrocatalytic applications. The ME voltage coefficients, which express the efficiency of energy conversion from magnetic field to electricity, show differences depending on the setup for ME measurements of MENPs. Therefore, numerous attempts have been made to optimize the ME characterization method to reduce measurement errors resulting from charge leakages caused by the specimen preparation, as well as to investigate the ME effect of a single nanoparticle. Our review is focused on the structures, syntheses (hydrothermal and sol–gel methods), activation mechanism, and measurement of magnetoelectricity, as well as applications, of core–shell MENPs. Multiferroics ; Materials science ; Piezoelectricity ; Nanotechnology ; Nanoparticle ; Magnetic nanoparticles ; Magnetostriction ; Characterization (materials science) ; Magnetic field ; Optoelectronics ; Ferroelectricity ; Dielectric ; Composite material ; Physics Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Dielectric properties of ceramics
Authors: Jamal‐Deen Musah | A. M. Ilyas | S. Venkatesh | Solomon Mensah | Samuel K. Kwofie | Vellaisamy A. L. Roy | Chi‐Man Lawrence Wu
Source: Nano Research Energy (review, 2022-09-29)
Field: Materials Science — Materials Chemistry

Isovalent substitution in metal chalcogenide materials for improving thermoelectric power generation – A critical review The adverse effect of fossil fuels on the environment is driving research to explore alternative energy sources. Research studies have demonstrated that renewables can offer a promising strategy to curb the problem, among which thermoelectric technology stands tall. However, the challenge with thermoelectric materials comes from the conflicting property of the Seebeck coefficient and the electrical conductivity resulting in a low power factor and hence a lower figure of merit. Researchers have reported various techniques to enhance the figure of merit, particularly in metal chalcogenide thermoelectric materials. Here we present a review on isovalent substitution as a tool to decouple the interdependency of the electrical conductivity and Seebeck coefficient to facilitate simultaneous enhancement in these two parameters. This is proven true in both cationic and anionic side substitutions in metal chalcogenide thermoelectric materials. Numerous publications relating to isovalent substitution in metal chalcogenide thermoelectric are reviewed. This will serve as a direction for current and future research to enhance thermoelectric performance and device application. This review substantiates the role of isovalent substitution in enhancing metal chalcogenide thermoelectric properties compared with conventional systems. Chalcogenide ; Thermoelectric effect ; Thermoelectric materials ; Seebeck coefficient ; Materials science ; Figure of merit ; Thermoelectric generator ; Engineering physics ; Substitution (logic) ; Thermal conductivity ; Optoelectronics ; Computer science ; Thermodynamics ; Composite material ; Engineering ; Physics Advanced Thermoelectric Materials and Devices ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films