jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Janelle P. Wharry | Caleb D. Clement | Yangyang Zhao | Katelyn Baird | D. Frazer | Jatuporn Burns | Yu Lu | Yaqiao Wu | Collin Knight | Donna Post Guillen | David Gandy
Source: Data in Brief (article, 2023-03-25)
Field: Materials Science — Materials Chemistry

Mechanical testing data from neutron irradiations of PM-HIP and conventionally manufactured nuclear structural alloys This article presents the comprehensive mechanical testing data archive from a neutron irradiation campaign of nuclear structural alloys fabricated by powder metallurgy with hot isostatic pressing (PM-HIP). The irradiation campaign was designed to facilitate a direct comparison of PM-HIP to conventional casting or forging. Five common nuclear structural alloys were included in the campaign: 316L stainless steel, SA508 pressure vessel steel, Grade 91 ferritic steel, and Ni-base alloys 625 and 690. Irradiations were carried out in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) to target doses of 1 and 3 displacements per atom (dpa) at target temperatures of 300 and 400 °C. This article contains the data collected from post-irradiation uniaxial tensile tests following ASTM E8 specifications, fractography of these tensile bars, and nanoindentation. By making this systematic and valuable neutron irradiated mechanical behavior dataset openly available to the nuclear materials research community, researchers may now use this data to populate material performance databases, validate material performance and hardening models, design follow-on experiments, and enable future nuclear code-qualification of PM-HIP techniques. Hot isostatic pressing ; Materials science ; Ultimate tensile strength ; Nanoindentation ; Neutron ; Hardening (computing) ; Tensile testing ; Irradiation ; Nuclear engineering ; Metallurgy ; Composite material ; Microstructure ; Engineering ; Nuclear physics Fusion materials and technologies ; Nuclear Materials and Properties ; Advanced materials and composites
Authors: Mohammad Reza Sadeghi | S. Mohammad Hosseini Varkiyani | Ali Asghar Asgharian Jeddi
Source: Scientific Reports (article, 2023-10-17)
Field: Materials Science — Polymers and Plastics

Machine learning in optimization of nonwoven fabric bending rigidity in spunlace production line Spunlace nonwoven fabrics have been extensively employed in different applications such as medical, hygienic, and industrial due to their drapeability, soft handle, low cost, and uniform appearance. To manufacture a spunlace nonwoven fabric with desirable properties, production parameters play an important role. Moreover, the relationship between the primary response and input parameter and the relationship between the secondary response and primary responses of spunlace nonwoven fabric were modeled via an artificial neural network (ANN). Furthermore, a multi-objective optimization via genetic algorithm (GA) to find a combination of production parameters to fabricate a sample with the highest bending rigidity and lowest basis weight was carried out. The results of optimization showed that the cost value of the best sample is 0.373. The optimized set of production factors were Young's modulus of fiber of 0.4195 GPa, the line speed of 53.91 m/min, the average pressure of water jet 42.43 bar, and the feed rate of 219.67 kg/h, which resulted in bending rigidity of 1.43 mN [Formula: see text]/cm and basis weight of 37.5 gsm. In terms of advancing the textile industry, it is hoped that this work provides insight into engineering the final properties of spunlace nonwoven fabric via the implementation of machine learning. Bending ; Materials science ; Artificial neural network ; Computer science ; Rigidity (electromagnetism) ; Production line ; Algorithm ; Composite material ; Bridging (networking) ; Artificial intelligence ; Machine learning ; Mechanical engineering ; Engineering Textile materials and evaluations ; Additive Manufacturing and 3D Printing Technologies ; Advanced Sensor and Energy Harvesting Materials
Authors: Ulrich E. Prechsl | Abraham Mejia‐Aguilar | Cameron Brodrick Cullinan
Source: Scientific Reports (article, 2023-09-22)
Field: Chemistry — Analytical Chemistry

In vivo spectroscopy and machine learning for the early detection and classification of different stresses in apple trees The use of in vivo spectroscopy to detect plant stress in its early stages has the potential to enhance food safety and reduce the need for plant protection products. However, differentiating between various stress types before symptoms appear remains poorly studied. In this study, we investigated the potential of Vis-NIR spectroscopy to differentiate between stress types in apple trees (Malus x domestica Borkh.) exposed to apple scab, waterlogging, and herbicides in a greenhouse. Using a spectroradiometer, we collected spectral signatures of leaves still attached to the tree and utilized machine learning techniques to develop predictive models for detecting stress presence and classifying stress type as early as 1-5 days after exposure. Our findings suggest that changes in spectral reflectance at multiple regions accurately differentiate various types of plant stress on apple trees. Our models were highly accurate (accuracies between 0.94 and 1) when detecting the general presence of stress at an early stage. The wavelengths important for classification relate to photosynthesis via pigment functioning (684 nm) and leaf water (~ 1800-1900 nm), which may be associated with altered gas exchange as a short-term stress response. Overall, our study demonstrates the potential of spectral technology and machine learning for early diagnosis of plant stress, which could lead to reduced environmental burden through optimizing resource utilization in agriculture. Malus ; Machine learning ; Stress (linguistics) ; Artificial intelligence ; Horticulture ; Computer science ; Environmental science ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Psidium guajava Extracts and Applications
Authors: O. Williams | Olivia Rusli | Lida Ezzedinloo | Tyren M. Dodgen | Jack K. Clegg | Nicole J. Rijs
Source: Angewandte Chemie International Edition (article, 2023-11-28)
Field: Chemistry — Spectroscopy

Automated Structural Activity Screening of β‐Diketonate Assemblies with High‐Throughput Ion Mobility‐Mass Spectrometry Embracing complexity in design, metallo-supramolecular self-assembly presents an opportunity for fabricating materials of economic significance. The array of accessible supramolecules is alluring, along with favourable energy requirements. Implementation is hampered by an inability to efficiently characterise complex mixtures. The stoichiometry, size, shape, guest binding properties and reactivity of individual components and combinations thereof are inherently challenging to resolve. A large combinatorial library of four transition metals (Fe, Cu, Ni and Zn), and six β-diketonate ligands at different molar ratios and pH was robotically prepared and directly analysed over multiple timepoints with electrospray ionisation travelling wave ion mobility-mass spectrometry. The dataset was parsed for self-assembling activity without first attempting to structurally assign individual species. Self-assembling systems were readily categorised without manual data-handling, allowing efficient screening of self-assembly activity. This workflow clarifies solution phase supramolecular assembly processes without manual, bottom-up processing. The complex behaviour of the self-assembling systems was reduced to simpler qualities, which could be automatically processed. Supramolecular chemistry ; Mass spectrometry ; Ion-mobility spectrometry ; Workflow ; Electrospray ; Electrospray ionization ; Stoichiometry ; Molar mass ; Nanotechnology ; Chemistry ; Characterization (materials science) ; Self-assembly ; Ion ; Computer science ; Combinatorial chemistry ; Materials science ; Polymer ; Molecule ; Chromatography ; Database ; Organic chemistry Mass Spectrometry Techniques and Applications ; Molecular Sensors and Ion Detection ; Supramolecular Chemistry and Complexes
Authors: Abderrahim Khamlichi | Fernando Garnacho | Pascual Simón
Source: Sensors (article, 2023-06-27)
Field: Materials Science — Materials Chemistry

New Synthetic Partial Discharge Calibrator for Qualification of Partial Discharge Analyzers for Insulation Diagnosis of HVDC and HVAC Grids A synthetic partial discharge (PD) calibrator has been developed to qualify PD analyzers used for insulation diagnosis of HVAC and HVDC grids including cable systems, AIS, GIS, GIL, power transformers, and HVDC converters. PD analyzers that use high-frequency current transformers (HFCT) can be qualified by means of the metrological and diagnosis tests arranged in this calibrator. This synthetic PD calibrator can reproduce PD pulse trains of the same sequence as actual representative defects (cavity, surface, floating potential, corona, SF<sub>6</sub> protrusion, SF<sub>6</sub> jumping particles, bubbles in oil, etc.) acquired in HV equipment in service or by means of measurements made in HV laboratory test cells. The diagnostic capabilities and PD measurement errors of the PD analyzers using HFCT sensors can be determined. A new time parameter, "PD Time", associated with any arbitrary PD current pulse <i>i(t)</i> is introduced for calibration purposes. It is defined as the equivalent width of a rectangular PD pulse with the same charge value and amplitude as the actual PD current pulse. The synthetic PD calibrator consists of a pulse generator that operates on a current loop matched to 50 Ω impedance to avoid unwanted reflections. The injected current is measured by a reference measurement system built into the PD calibrator that uses two HFCT sensors to ensure that the current signal is the same at the input and output of the calibration cage where the HFCT of the PD analyzer is being calibrated. Signal reconstruction of the HFCT output signal to achieve the input signal is achieved by applying state variable theory using the transfer impedance of the HFCT sensor in the frequency domain. Partial discharge ; Current transformer ; Calibration ; Spectrum analyzer ; Transformer ; Electrical engineering ; SIGNAL (programming language) ; Rogowski coil ; Converters ; Metrology ; Electronic engineering ; Engineering ; Voltage ; Acoustics ; Current (fluid) ; Computer science ; Physics ; Optics High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Power Transformer Diagnostics and Insulation
Authors: Genbao Liu | Yutao Guo | Zhiyu Jian | Mojia Huang | Tengfei Zhao
Source: Crystals (article, 2023-01-05)
Field: Chemistry — Spectroscopy

Experimental Study on the Thermal Conductivity of Improved Graphite Composite Insulation Boards The thermal conductivity of thermal insulation materials directly affects the building energy consumption. The types and constituents of thermal insulation materials in thermal insulation boards are the key to determining the insulation performance. By optimizing the material constituents and ratios, this paper proposes an improved graphite composite insulation board (GCIB), which has lower thermal conductivity and good fire resistance. Through theoretical derivation, it is found that the limit range of the thermal conductivity of the new GCIB is 0.042–0.064 W/(m · K). Combined with the results of theoretical value analysis, and according to the ratios of material components, the random distribution function of each material component is constructed, and the numerical model of GCIB is established. Through numerical analysis, the range of thermal conductivity of the new composite insulation board is 0.046–0.050 W/(m · K). Finally, we establish an experimental model of the new GCIB. Through the model test of six GCIBs, the thermal conductivity of the new GCIB is obtained as 0.046 W/(m · K), which is in good agreement with the results of theoretical analysis and numerical simulation. Through theoretical analysis, numerical simulation and a sample test, this paper verifies the better thermal insulation performance of the improved GCIB, providing theoretical and numerical simulation methods for the new GCIB, as well as a theoretical reference for the promotion and application of the GCIB. Thermal conductivity ; Materials science ; Thermal insulation ; Composite material ; Graphite ; Composite number ; Thermal ; Thermodynamics ; Physics Aerogels and thermal insulation ; Advanced Cellulose Research Studies ; Hygrothermal properties of building materials
Authors: Lizhuo Ai | Aleksandra Binek | Simion Kreimer | Matthew Ayres | Aleksandr Stotland | Jennifer E. Van Eyk
Source: Journal of Proteome Research (article, 2023-04-11)
Field: Chemistry — Spectroscopy

High-Field Asymmetric Waveform Ion Mobility Spectrometry: Practical Alternative for Cardiac Proteome Sample Processing Heart tissue sample preparation for mass spectrometry (MS) analysis that includes prefractionation reduces the cellular protein dynamic range and increases the relative abundance of nonsarcomeric proteins. We previously described "IN-Sequence" (IN-Seq) where heart tissue lysate is sequentially partitioned into three subcellular fractions to increase the proteome coverage more than a single direct tissue analysis by mass spectrometry. Here, we report an adaptation of the high-field asymmetric ion mobility spectrometry (FAIMS) coupled to mass spectrometry, and the establishment of a simple one step sample preparation coupled with gas-phase fractionation. The FAIMS approach substantially reduces manual sample handling, significantly shortens the MS instrument processing time, and produces unique protein identification and quantification approximating the commonly used IN-Seq method in less time. Ion-mobility spectrometry ; Proteome ; Mass spectrometry ; Chemistry ; Chromatography ; Fractionation ; Proteomics ; Sample preparation ; Analytical Chemistry (journal) ; Sample (material) ; Biochemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Simon A. Haughey | Holly Montgomery | Bernadette Moser | Natasha Logan | Christopher T. Elliott
Source: Foods (article, 2023-12-16)
Field: Chemistry — Analytical Chemistry

Utilization of Hyperspectral Imaging with Chemometrics to Assess Beef Maturity There is a growing demand from consumers for more assurance in premium food products such as beef and especially steak. The quality of beef steak is primarily dictated by the maturation which ultimately influences its taste and flavor. These enhanced qualities have resulted in steak becoming a premium product that consumers are willing to pay a premium price for. A challenge, however, is analyzing the maturity of beef by traditional analytical techniques. Hyperspectral imaging (HSI) is a methodology that is gaining traction mainly due to miniaturization, improved optics, and software. In this study, HSI was applied to wet aged beef supplied at various stages of maturity, with spectral data generated using a portable hyperspectral camera. Two trials were conducted over a five-month period: (i) proof of principle and (ii) a bespoke sampling trial for the industry. With the support of industry participation, all samples were sourced from a highly reputable UK/Ireland supplier. To enhance data interpretation, the spectral data collected were combined with multivariate analysis. A range of chemometric models were generated using unsupervised and supervised methods to determine the maturity of the beef, and external validation was performed. The external validation showed good accuracy for "unknown samples" tested against the model set and ranged from 74 to 100% for the different stages of maturity (20, 30, and 40 days old). This study demonstrated that HSI can detect different maturity timepoints for beef samples, which could play an important role in solving some of the challenges that the industry faces with ensuring the authenticity of their products. This is the first time that portable HSI has been coupled with chemometric modeling for assessing the maturity of beef, and it can serve as a model for other food authenticity and quality applications. Hyperspectral imaging ; Chemometrics ; Maturity (psychological) ; Quality (philosophy) ; Capability Maturity Model ; Quality assurance ; Computer science ; Mathematics ; Environmental science ; Marketing ; Business ; Artificial intelligence ; Machine learning ; Software ; Service (business) ; Psychology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Sipeng Wu | Xing Zhong | Z. H. Qu | Yuanhang Wang | Lei Li | Chaoli Zeng
Source: Photonics (article, 2023-04-24)
Field: Chemistry — Spectroscopy

Infrared Gas Detection and Concentration Inversion Based on Dual-Temperature Background Points Gas detection based on infrared thermal imaging is applied in many areas, but it is generally applied as a qualitative detection method to observe the target area; on the other hand, quantitative research on gas concentration is less common, the measurement accuracy is poor, and the calculation method of concentration in the commonly adopted transmission model is also complicated. In this paper, based on the radiance transfer model of gas infrared imaging technology, the influence of gas concentration, gas temperature, and background temperature on gas imaging detection is investigated, a gas detection and concentration inversion method based on dual-temperature background points is proposed, and the effects of the choice of reference band on background temperature correction are analyzed in relation to the changing trend of dual-band radiance difference. To verify the effectiveness of this method, a gas detection system with dual-temperature background spots was constructed in this paper utilizing a cooled mid-wave infrared focal plane detector plus a reference filter and a measurement filter, which achieved a promising concentration accuracy of less than 10% for carbon dioxide at a detectable range. Meanwhile, an infrared imaging system with a noise equivalent temperature difference (NETD) of 40 mK was employed to simulate the detection of methane, which enables the detection and concentration inversion of methane gas at a minimum concentration of 500 ppm·m at a distance of 1 km, which proves the capability of long-range detection. Radiance ; Methane ; Infrared ; Detector ; Materials science ; Optics ; Inversion (geology) ; Temperature measurement ; Environmental science ; Analytical Chemistry (journal) ; Remote sensing ; Physics ; Chemistry ; Geology Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Advanced Chemical Sensor Technologies
Authors: Caterina Gabriele | Federica Aracri | Licia Elvira Prestagiacomo | Maria Antonietta Rota | Stefano Alba | Giuseppe Tradigo | Pietro Hiram Guzzi | Giovanni Cuda | Rocco Damiano | Pierangelo Veltri | Marco Gaspari
Source: Clinical Proteomics (article, 2023-11-21)
Field: Chemistry — Spectroscopy

Development of a predictive model to distinguish prostate cancer from benign prostatic hyperplasia by integrating serum glycoproteomics and clinical variables To address the impelling need of more sensitive and specific serum diagnostic tests, a predictive model combining proteomic and clinical variables was developed. A preliminary evaluation to build a new tool able to discriminate aggressive presentations of PCa from tumors with benign behavior was exploited. This predictor displayed moderate performances, but no conclusions can be drawn due to the limited number of the sample cohort. Data are available via ProteomeXchange with identifier PXD035935. Prostate cancer ; Receiver operating characteristic ; Medicine ; Biomarker discovery ; Biomarker ; Oncology ; Cancer ; Prostate-specific antigen ; Glycoproteomics ; Prostate ; Internal medicine ; Proteomics ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Glycosylation and Glycoproteins Research
Authors: Yizhi Sheng | Wanjun Jiang | Min Zhang
Source: Applied Sciences (article, 2023-10-19)
Field: Chemistry — Inorganic Chemistry

Mobilization, Speciation, and Transformation of Organic and Inorganic Contaminants in Soil–Groundwater Ecosystems The delicate balance of our ecosystems is under threat from the unrelenting release of contaminants into the environment [...] Environmental science ; Environmental chemistry ; Groundwater ; Genetic algorithm ; Ecology ; Chemistry ; Geology ; Biology ; Geotechnical engineering Radioactive element chemistry and processing ; Pesticide and Herbicide Environmental Studies ; Heavy metals in environment
Authors: Janne Kool | A. Evenhuis
Source: Smart Agricultural Technology (article, 2023-10-05)
Field: Chemistry — Analytical Chemistry

Early detection of Phytophthora infestans in potato plants using hyperspectral imaging, local comparison and a convolutional neural network An important primary inoculum source of Phytophthora infestans are latent infected seed potatoes. To remove those inoculum sources, it is necessary to detect these plants before symptom expression. Often, when a human can detect it by eye, sporulation has occurred and secondary spread might have commenced. Moreover, it is not feasible to check all potato plants by manual labour. It is possible to detect a P. infestans infection early, using hyperspectral imaging, however, models trained on the spectral information only, do not generalize, because the spectrum is very much dependent on the local conditions like soil humidity. A method is developed to detect P. infestans early by comparing spectra from infected plants with the spectra of healthy plants that have grown under similar conditions, i.e., in the same plant row, and creating an image that quantifies deviations. On those images in turn a convolutional neural network is trained, which is able to detect infections on plants grown in another field or a year later in a similar manner but under different conditions. The method has been tested in the field in a dry year with little potato late blight infection. In this experiment it has not been possible to detect the infection using hyperspectral imaging. Phytophthora infestans ; Hyperspectral imaging ; Blight ; Convolutional neural network ; Biology ; Horticulture ; Artificial intelligence ; Pattern recognition (psychology) ; Botany ; Computer science Spectroscopy and Chemometric Analyses ; Plant Pathogens and Resistance ; Remote Sensing in Agriculture