jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yiming﻿ Cao﻿ | Yan Zhang | Qi Wang | Ran Zhao | Mingxi Hou | Shuang-Ting Yu | Kai-Kuo Wang | Ying‐Jie Chen | Xiaoqing Sun | Shijing Liu | Jiong-Tang Li
Source: Current Research in Food Science (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Skin hyperspectral imaging and machine learning to accurately predict the muscular poly-unsaturated fatty acids contents in fish The polyunsaturated fatty acids (PUFAs), particularly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are critical determinants of the nutritional quality of fish. To rapidly and non-destructively determine the muscular PUFAs in living fish, an accuracy technique is urgently needed. In this study, we combined skin hyperspectral imaging (HSI) and machine learning (ML) methods to assess the muscular PUFAs contents of common carp. Hyperspectral images of the live fish skin were acquired in the 400-1000 nm spectral range. The spectral data were preprocessed using Savitzky-Golay (SG), multivariate scattering correction (MSC), and standard normal variable (SNV) methods, respectively. The competitive adaptive reweighted sampling (CARS) method was applied to extract the optimal wavelengths. With the skin spectra of fish, five ML methods, including the extreme learning machine (ELM), random forest (RF), radial basis function (RBF), back propagation (BP), and least squares support vector machine (LS-SVM) methods, were used to predict the PUFAs and EPA + DHA contents. With the spectral data processed with the SG, the RBF model achieved outstanding performance in predicting the EPA + DHA and PUFAs contents, yielding coefficients of determination (R<sup>2</sup> <sub>P</sub>) of 0.9914 and 0.9914, root mean square error (RMSE) of 0.3352 and 0.3346, and mean absolute error (MAE) of 0.2659 and 0.2660, respectively. Finally, the visualization distribution maps under the optimal model would facilitate the direct determination of the fillet PUFAs and EPA + DHA contents. The combination of skin HSI and the optimal ML method would be promising to rapidly select living fish having high muscular PUFAs contents. Hyperspectral imaging ; Fish <Actinopterygii> ; Computer science ; Artificial intelligence ; Biology ; Fishery Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Thomas Galvani | Ali Hamze | Laura Caputo | Onurcan Kaya | Simon M.‐M. Dubois | Luigi Colombo | Việt Hùng Nguyễn | Yongwoo Shin | Hyeon‐Jin Shin | Jean‐Christophe Charlier | Stephan Roche
Source: Journal of Physics Materials (article, 2024-05-15)
Field: Materials Science — Materials Chemistry

Exploring dielectric properties in atomistic models of amorphous boron nitride Abstract We report a theoretical study of dielectric properties of models of amorphous Boron Nitride, using interatomic potentials generated by machine learning. We first perform first-principles simulations on small (about 100 atoms in the periodic cell) sample sizes to explore the emergence of mid-gap states and its correlation with structural features. Next, by using a simplified tight-binding electronic model, we analyse the dielectric functions for complex three dimensional models (containing about 10.000 atoms) embedding varying concentrations of sp 1 , sp 2 and sp 3 bonds between B and N atoms. Within the limits of these methodologies, the resulting value of the zero-frequency dielectric constant is shown to be influenced by the population density of such mid-gap states and their localization characteristics. We observe nontrivial correlations between the structure-induced electronic fluctuations and the resulting dielectric constant values. Our findings are however just a first step in the quest of accessing fully accurate dielectric properties of as-grown amorphous BN of relevance for interconnect technologies and beyond. Dielectric ; Boron nitride ; Amorphous solid ; Materials science ; Condensed matter physics ; Density of states ; Statistical physics ; Computational chemistry ; Molecular physics ; Nanotechnology ; Chemistry ; Optoelectronics ; Physics ; Crystallography Graphene research and applications ; Semiconductor materials and devices ; Thermal properties of materials
Authors: Yoonwoo Kim | Jeong‐Wan Jo | Jiajie Yang | Bernstein Yaron | Sanghyo Lee | Sung‐Min Jung | Jong Min Kim
Source: Advanced Optical Materials (article, 2024-02-11)
Field: Materials Science — Materials Chemistry

Computational Characterization of Quantum‐Dot Light‐Emitting Diodes by Combinatorial Exciton Recombination Parameters and Photon Extraction Efficiency Abstract Quantum‐dot light‐emitting diodes (QD‐LEDs) have gained significant attention for next‐generation display and lighting systems owing to their superior color selectivity and color purity. To maximize the efficiency of QD‐LED devices, it is of great importance to identify the key factors that govern their electro–optical properties. The efficiency of QD‐LED devices is strongly influenced by combinatorial processes, represented by the Shockley‐Read‐Hall rate A , Langevin strength B , and Auger probability C (ABC parameters) of quantum‐dots (QDs), along with photon extraction efficiency of QD‐LED devices. In this study, an integrated computational framework is proposed to accurately analyze the electro–optical properties of QD‐LED devices. The experimental device properties are characterized by ABC and photon extraction efficiency parameters through an innovative numerical data‐fitting procedure. Utilizing these parameters, a parametric analysis is performed based on a complete computational charge transport simulation model to explore the influence of the combinatorial exciton recombination processes. This computational framework aligns excellently with experimental results, showcasing its remarkable reliability and effectiveness in both quantitatively characterizing QD nanoparticles and in the detailed analysis of the electro–optical properties of QD‐LED devices. Quantum dot ; Materials science ; Diode ; Optoelectronics ; Light-emitting diode ; Auger effect ; Photon ; Exciton ; Optics ; Physics ; Quantum mechanics Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Semiconductor Quantum Structures and Devices
Authors: Victor Poline | Ravi Raj Purohit Purushottam Raj Purohit | P. Bordet | Nils Blanc | Pauline Martinetto
Source: Journal of Applied Crystallography (article, 2024-05-31)
Field: Materials Science — Materials Chemistry

Neural networks for rapid phase quantification of cultural heritage X-ray powder diffraction data Recent developments in synchrotron radiation facilities have increased the amount of data generated during acquisitions considerably, requiring fast and efficient data processing techniques. Here, the application of dense neural networks (DNNs) to data treatment of X-ray diffraction computed tomography (XRD-CT) experiments is presented. Processing involves mapping the phases in a tomographic slice by predicting the phase fraction in each individual pixel. DNNs were trained on sets of calculated XRD patterns generated using a Python algorithm developed in-house. An initial Rietveld refinement of the tomographic slice sum pattern provides additional information (peak widths and integrated intensities for each phase) to improve the generation of simulated patterns and make them closer to real data. A grid search was used to optimize the network architecture and demonstrated that a single fully connected dense layer was sufficient to accurately determine phase proportions. This DNN was used on the XRD-CT acquisition of a mock-up and a historical sample of highly heterogeneous multi-layered decoration of a late medieval statue, called 'applied brocade'. The phase maps predicted by the DNN were in good agreement with other methods, such as non-negative matrix factorization and serial Rietveld refinements performed with <i>TOPAS</i>, and outperformed them in terms of speed and efficiency. The method was evaluated by regenerating experimental patterns from predictions and using the <i>R</i>-weighted profile as the agreement factor. This assessment allowed us to confirm the accuracy of the results. Python (programming language) ; Rietveld refinement ; Computer science ; Phase (matter) ; Materials science ; Artificial neural network ; Synchrotron radiation ; Diffraction ; Optics ; Algorithm ; Artificial intelligence ; Physics X-ray Diffraction in Crystallography ; Cultural Heritage Materials Analysis ; Nuclear Physics and Applications
Authors: Mohammad Bagheri | Hannu‐Pekka Komsa
Source: Journal of Raman Spectroscopy (article, 2024-06-03)
Field: Materials Science — Materials Chemistry

Structure–spectrum relationship in the calculated Raman spectra of silicates Abstract Silicate minerals have a rich structural variety consisting of silicon oxide clusters or networks of any dimensionality interdispersed with different types of elements, which is reflected in marked changes in the Raman spectra. Understanding how the changes in the Raman spectra are correlated with the atomic structure would be highly desirable for fast material identification and analysis. Extracting such trends from experimental spectra can be difficult owing to the uncertainties in the structural details of the samples and in ensuring consistency between measurements from different sources. Simulated spectra, however, avoid these problems, making them a good candidate for systematic studies. Here, we study the correlation between the structure and Raman spectral features of 179 silicates derived from a database of Raman spectra simulated using first‐principles calculations. We investigate the spectral similarities with a specific emphasis on materials containing isolated 0D clusters in nesosilicate (SiO 4 ), sorosilicate (Si 2 O 7 ), and cyclosilicate (Si 3 O 9 and Si 4 O 12 ) configurations. While trends identified in the previous reports can be confirmed, we find that the variations within each group of similar structural motifs tend to be larger than the changes across groups, and therefore, developing a reliable automated classification algorithm is likely to be challenging. Raman spectroscopy ; Spectrum (functional analysis) ; Spectral line ; Analytical Chemistry (journal) ; Chemistry ; Materials science ; Mineralogy ; Geology ; Nuclear magnetic resonance ; Physics ; Optics ; Environmental chemistry ; Astronomy X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques
Authors: Devrim Acehan | Katherine A. Spoth | Gabrielle R. Budziszewski | M. Elizabeth Snell | Christopher S. Campomizzi | Miranda L. Lynch | Sarah Bowman
Source: Cell Reports Physical Science (article, 2024-05-23)
Field: Materials Science — Materials Chemistry

Reaching the potential of electron diffraction Microcrystal electron diffraction (MicroED) is an emerging structural technique in which submicron crystals are used to generate diffraction data for structural studies. Structures allow for the study of molecular-level architecture and drive hypotheses about modes of action, mechanisms, dynamics, and interactions with other molecules. Combining cryoelectron microscopy (cryo-EM) instrumentation with crystallographic techniques, MicroED has led to three-dimensional structural models of small molecules, peptides, and proteins and has generated tremendous interest due to its ability to use vanishingly small crystals. In this perspective, we describe the current state of the field for MicroED methodologies, including making and detecting crystals of the appropriate size for the technique, as well as ways to best handle and characterize these crystals. Our perspective provides insight into ways to unlock the full range of potential for MicroED to access previously intractable samples and describes areas of future development. Nanotechnology ; Diffraction ; Cryo-electron microscopy ; Electron diffraction ; Materials science ; Chemistry ; Physics ; Optics Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; RNA and protein synthesis mechanisms
Authors: T Sarker | Robert C. Deen | Dilip Kumar Ghosh | M.N.H. Mia | MM Rahman | MA Hashem
Source: Meat Research | ISSN (Online Version) 2790-1971 (review, 2024-12-31)
Field: Chemistry — Analytical Chemistry

AI driven approach and NIRS: A review on meat quality and safety Consumers' increasing demand for high-quality and safe meat products has led the food industry to explore advanced analytical techniques, such as artificial intelligence and nearinfrared reflectance spectroscopy to assess and monitor the quality and safety of meat. The quality and safety of meat products have become increasingly important considerations for consumers, driven by concerns over health implications and the need for transparency in the meat supply chain. The meat industry faces increasing demands from consumers for higher quality and safer products, driven by concerns over food-borne illnesses and the nutritional value of meat. Addressing these growing consumer expectations has become a priority, prompting researchers to actively investigate the potential of innovative nondestructive techniques, including near-infrared spectroscopy and others in combination with advanced data analysis methodologies to enable rapid, objective, and environmentally-friendly assessment of diverse meat quality and safety attributes. This review examines the current state of research on the application of these technologies in the meat industry. Additionally, it explores the integration of artificial intelligence algorithms with near-infrared spectroscopy data to enhance the accuracy and reliability of meat quality prediction and authentication. Quality (philosophy) ; Computer science ; Food science ; Business ; Chemistry ; Philosophy ; Epistemology Spectroscopy and Chemometric Analyses
Authors: Nishant Shirodkar | Viswajit Talluru | Gary D. Seidel
Source: Smart Materials and Structures (article, 2024-09-27)
Field: Materials Science — Materials Chemistry

Damage sensing in multi-functional nanocomposite polymer bonded energetics with embedded multi-walled carbon nanotube sensing networks Abstract This experimental investigation evaluates the strain and damage sensing abilities of multi-walled carbon nanotube (MWCNT) networks embedded in the binder phase of polymer-bonded energetics (PBEs). PBEs are a special class of particulate composite materials that consist of energetic crystals bound by a polymer matrix, wherein the polymer matrix serves to maintain the composite’s shape and form. The structural health monitoring (SHM) approach presented in this work exploits the piezoresistive properties of the distributed MWCNT networks. Major challenges faced during such implementation include the low binder concentrations of PBEs, the presence of conductive/non-conductive particulate phases, the high degree of heterogeneity in the PBE microstructure, and achieving the optimal MWCNT dispersion. In this study, ammonium perchlorate (AP) crystals as the oxidizer, Aluminum grains as the metallic fuel, and Polydimethylsiloxane (PDMS) as the binder are used as the constituents for fabricating PBEs. To study the effect of each constituent on the MWCNT network’s SHM abilities, various materials systems are comprehensively studied: MWCNT/PDMS materials are first evaluated to study the binder’s electromechanical response, followed by AP/MWCNT/PDMS to assess the impact of AP addition, and finally, AP/AL/MWCNT/PDMS to evaluate the impact of adding conductive aluminum grains. Compression samples (ASTM D695) were fabricated and subjected to monotonic compression. Electrical resistance is recorded in conjunction with the mechanical test via an LCR meter. Gauge factors relating to the change in normalized resistance to applied strain are calculated to quantify the electromechanical response. MWCNT dispersions and mechanical failure modes are analyzed via scanning electron microscopy imaging of the fracture surfaces. Correlations between the electrical behavior in response to the mechanical behavior are presented, and possible mechanisms that influence the electromechanical behavior are discussed. The results presented herein demonstrate the successful ability of MWCNT networks as SHM sensors capable of real-time strain and damage assessment of PBEs. Carbon nanotube ; Nanocomposite ; Materials science ; Energetics ; Polymer ; Composite material ; Nanotechnology ; Polymer nanocomposite ; Nanotube ; Physics ; Thermodynamics Machine Learning in Materials Science ; Carbon Nanotubes in Composites
Authors: Ismael Rodríguez Palomo | Bharath Nair | Yun Chiang | Joannes Dekker | Benjamin Dartigues | Meaghan Mackie | Miranda Evans | Ruairidh Macleod | Jesper V. Olsen | Matthew J. Collins
Source: Peer Community Journal (article, 2024-11-21)
Field: Chemistry — Spectroscopy

Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins Palaeoproteomics is a rapidly evolving discipline, and practitioners are constantly developing novel strategies for the analyses and interpretations of complex, degraded protein mixtures. The community has also established standards of good practice to interrogate our data. However, there is a lack of a systematic exploration of how these affect the identification of peptides, post-translational modifications (PTMs), proteins and their significance (through the False Discovery Rate) and correctness. We systematically investigated the performance of a wide range of sequencing tools and search engines in a controlled system: the experimental degradation of the single purified bovine β-lactoglobulin (BLG), heated at 95°C and pH 7 for 0, 4 and 128 days. We target BLG since it is one of the most robust and ubiquitous proteins in the archaeological record. We tested different reference database choices, a targeted dairy protein one, and the whole bovine proteome and the three digestion options (tryptic-, semi-tryptic- and non-specific searches), in order to evaluate the effects of search space and the identification of peptides. We also explored alternative strategies, including open search that allows for the global identification of PTMs based upon wide precursor mass tolerance and de novo sequencing to boost sequence coverage. We analysed the samples using Mascot, MaxQuant, Metamorpheus, pFind, Fragpipe and DeNovoGUI (pepNovo+, DirecTag, Novor), benchmarked these tools and discuss the optimal strategy for the characterisation of ancient proteins. We also studied physicochemical properties of the BLG that correlate with bias in the identification coverage. Benchmarking ; Identification (biology) ; Computational biology ; Computer science ; Data science ; Biology ; Business ; Ecology Advanced Proteomics Techniques and Applications ; Ubiquitin and proteasome pathways ; Protein Structure and Dynamics
Authors: Jonathan Vinícius Moreira Torquato | Carlos Bruno Barreto Luna | Edson Antônio dos Santos Filho | Emanuel Pereira do Nascimento | Tomás Jefférson Alves de Mélo | Renate Maria Ramos Wellen | Edcleide Maria Araújo | Dayanne Diniz de Souza Morais
Source: Polymers (article, 2024-11-05)
Field: Materials Science — Biomaterials

Repurposing ABS to Produce Polyamide 6 (PA6)-Based Blends: Reactive Compatibilization with SAN-g-MA of a High Degree of Functionalization In this study, recycled acrylonitrile-butadiene-styrene terpolymer (ABSr) was reused to produce polyamide 6 (PA6)-based blends. This was achieved through reactive compatibilization using styrene-acrylonitrile-maleic anhydride (SAN-g-MA) copolymer with a high degree of functionalization (6-10% MA). The PA6/ABSr and PA6/ABSr/SAN-g-MA blends were prepared through melt processing and injection molding and then analyzed for their rheological, mechanical, thermomechanical, thermal, and structural properties, as well as morphology. The torque rheometry revealed a maximum reactivity of the PA6/ABSr (70/30 wt%) blend with low SAN-g-MA (5 phr-parts per hundred resin) content, while above this threshold, torque began to decline, indicating compatibilizer saturation in the interface. These findings were further substantiated by the increase in complex viscosity and the lower melt flow index (MFI) of the PA6/ABSr/SAN-g-MA (5 phr) blend. The 5 phr SAN-g-MA reactive compatibilization of the PA6/ABSr blends significantly enhanced its impact strength, elongation at break, tensile strength, and heat deflection temperature (HDT) by 217%, 631%, 12.6%, and 9.5%, respectively, compared to PA6/ABSr. These findings are promising for the plastic recycling field, paving the way for the production of new tailor-made materials at a reduced price. Compatibilization ; Polyamide ; Surface modification ; Materials science ; Repurposing ; Polymer blend ; Composite material ; Chemical engineering ; Copolymer ; Polymer ; Engineering ; Waste management biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Polymer crystallization and properties
Authors: Chi Yao | Cheng‐tao Su | Ji‐ping Zou | Shang‐tao Ou‐yang | Jian Wu | Nan Chen | Yande Liu | Bin Li
Source: Journal of Chemometrics (article, 2024-05-21)
Field: Chemistry — Analytical Chemistry

Detection storage time of mangoes after mild bruise based on hyperspectral imaging combined with deep learning Abstract To reduce the number of bruised mangoes at source, it is important to determine the different storage times of mangoes after mild bruise. In order to address this issue, a hyperspectral imaging combined with deep learning model was proposed. First, the average spectrum of the sample bruised area was extracted as spectral features, and then, the six eigenvalues of the most representative PC1 image were calculated as texture features based on the gray level co‐occurrence matrix. In order to find the optimal discriminative model, random forest (RF), partial least squares discriminant analysis (PLS‐DA), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) models were built based on spectral features, texture features, and spectral features combined with texture features (Feature Fusion 1), respectively. The results showed that the best model discriminating model was based on CNN under Feature Fusion 1, with an overall accuracy of 90.22%. To reduce the redundant information and noise introduced by the full spectrum, uninformative variable elimination (UVE) and competitive adaptive reweighted sampling (CARS) algorithms were used to filter the spectral features. The screened spectral features were fused with texture features (Feature Fusion 2) and modeled again with RF, PLS‐DA, XGBoost, and CNN. The results showed that the optimal model for discriminating different storage times of mangoes after bruise was the CNN model based on feature fusion 2 (CARS), with an overall accuracy of 93.48%. In summary, this study shows that the spectral features combined with texture features can be used to effectively improve the model's discriminative results for different storage times of mango after mild bruise. Compared to other machine learning models, the CNN model in this paper achieves better results. It provides a theoretical basis for hyperspectral imaging combined with deep learning in discriminating different storage times of mangoes after mild bruise. Artificial intelligence ; Pattern recognition (psychology) ; Hyperspectral imaging ; Computer science ; Convolutional neural network ; Discriminative model ; Random forest ; Feature (linguistics) ; Mathematics Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Studies ; Bee Products Chemical Analysis