jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jingwei Zhang | Wei Lu | Jian Xing-liang | Qingying Hu | Dejian Dai
Source: Sensors (article, 2023-06-13)
Field: Chemistry — Analytical Chemistry

Nondestructive Detection of Egg Freshness Based on Infrared Thermal Imaging In this paper, we proposed a nondestructive detection method for egg freshness based on infrared thermal imaging technology. We studied the relationship between egg thermal infrared images (different shell colors and cleanliness levels) and egg freshness under heating conditions. Firstly, we established a finite element model of egg heat conduction to study the optimal heat excitation temperature and time. The relationship between the thermal infrared images of eggs after thermal excitation and egg freshness was further studied. Eight values of the center coordinates and radius of the egg circular edge as well as the long axis, short axis, and eccentric angle of the egg air cell were used as the characteristic parameters for egg freshness detection. After that, four egg freshness detection models, including decision tree, naive Bayes, k-nearest neighbors, and random forest, were constructed, with detection accuracies of 81.82%, 86.03%, 87.16%, and 92.32%, respectively. Finally, we introduced SegNet neural network image segmentation technology to segment the egg thermal infrared images. The SVM egg freshness detection model was established based on the eigenvalues extracted after segmentation. The test results showed that the accuracy of SegNet image segmentation was 98.87%, and the accuracy of egg freshness detection was 94.52%. The results also showed that infrared thermography combined with deep learning algorithms could detect egg freshness with an accuracy of over 94%, providing a new method and technical basis for online detection of egg freshness on industrial assembly lines. Thermography ; Infrared ; Artificial intelligence ; Thermal ; Nondestructive testing ; Egg white ; Segmentation ; Support vector machine ; Materials science ; Pattern recognition (psychology) ; Computer science ; Biological system ; Optics ; Chemistry ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Bee Products Chemical Analysis
Authors: Chen Shen | Jiachi Ye | Nicola Peserico | Yaliang Gui | Chaobo Dong | Haoyan Kang | Behrouz Movahhed Nouri | Hao Wang | Elham Heidari | Volker J. Sorger | Hamed Dalir
Source: Nanomaterials (article, 2023-07-19)
Field: Materials Science — Materials Chemistry

Enhancing Focusing and Defocusing Capabilities with a Dynamically Reconfigurable Metalens Utilizing Sb2Se3 Phase-Change Material Metalenses are emerging as an alternative to digital micromirror devices (DMDs), with the advantages of compactness and flexibility. The exploration of metalenses has ignited enthusiasm among optical engineers, positioning them as the forthcoming frontier in technology. In this paper, we advocate for the implementation of the phase-change material, Sb2Se3, capable of providing swift, reversible, non-volatile focusing and defocusing within the 1550 nm telecom spectrum. The lens, equipped with a robust ITO microheater, offers unparalleled functionality and constitutes a significant step toward dynamic metalenses that can be integrated with beamforming applications. After a meticulously conducted microfabrication process, we showcase a device capable of rapid tuning (0.1 MHz level) for metalens focusing and defocusing at C band communication, achieved by alternating the PCM state between the amorphous and crystalline states. The findings from the experiment show that the device has a high contrast ratio for switching of 28.7 dB. Materials science ; Flexibility (engineering) ; Lens (geology) ; Optoelectronics ; Reconfigurability ; Electronics ; Optics ; Microfabrication ; Phase change ; Phase (matter) ; Amorphous solid ; Computer science ; Nanotechnology ; Engineering physics ; Telecommunications ; Fabrication ; Physics ; Electrical engineering ; Engineering ; Chemistry Phase-change materials and chalcogenides ; Optical and Acousto-Optic Technologies ; Metamaterials and Metasurfaces Applications
Authors: Shaojie Tan | Songbai Cheng | Kai Wang | Xiaoxing Liu | Hui Cheng | Jun Wang
Source: Frontiers in Energy Research (article, 2023-03-10)
Field: Materials Science — Materials Chemistry

The development of micro and small modular reactor in the future energy market Micro and Small Modular Reactor (MSMR) is an emerging energy technology that meets the requirements of market demand, safety, efficiency, and sustainability. This paper summarizes the advantages, application scenarios, and advanced technologies to support MSMR. Now that the energy market is more flexible and the requirements are more complex, while MSMR can meet the market demand and has a lower cost compared with other clean energies such as wind and solar photovoltaic. The United States is vigorously developing MSMRs into residential energy markets. The MSMR developed around the world has more than three generations of safety characteristics that have adopted passive safety features. MSMR can be manufactured in the factory which reduces construction schedule, cost, and waste. The nuclear fuel supply chain for MSMR is complete and perfect, including the front end and back end. An increasing number of advanced technologies support the development of MSMR, including advanced materials (TRISO fuel and accident-tolerance fuel), advanced control knowledges (DI&amp;amp;C, cybersecurity, and AI), and an advanced computational platform (MOOSE framework). Schedule ; Modular design ; Factory (object-oriented programming) ; Engineering ; Supply and demand ; Environmental economics ; Computer science ; Economics Graphite, nuclear technology, radiation studies ; Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: Abraham O. Oluwole | Denis Shutin | Jani Reddy Bolla
Source: Essays in Biochemistry (review, 2023-02-21)
Field: Chemistry — Spectroscopy

Mass spectrometry of intact membrane proteins: shifting towards a more native-like context Integral membrane proteins are involved in a plethora of biological processes including cellular signalling, molecular transport, and catalysis. Many of these functions are mediated by non-covalent interactions with other proteins, substrates, metabolites, and surrounding lipids. Uncovering such interactions and deciphering their effect on protein activity is essential for understanding the regulatory mechanisms underlying integral membrane protein function. However, the detection of such dynamic complexes has proven to be challenging using traditional approaches in structural biology. Native mass spectrometry has emerged as a powerful technique for the structural characterisation of membrane proteins and their complexes, enabling the detection and identification of protein-binding partners. In this review, we discuss recent native mass spectrometry-based studies that have characterised non-covalent interactions of membrane proteins in the presence of detergents or membrane mimetics. We additionally highlight recent progress towards the study of membrane proteins within native membranes and provide our perspective on how these could be combined with recent developments in instrumentation to investigate increasingly complex biomolecular systems. Membrane protein ; Integral membrane protein ; Context (archaeology) ; Membrane ; Chemistry ; Mass spectrometry ; Function (biology) ; Biochemistry ; Protein–protein interaction ; Biological membrane ; Computational biology ; Biophysics ; Biology ; Cell biology ; Chromatography Mass Spectrometry Techniques and Applications ; Lipid Membrane Structure and Behavior ; Molecular Sensors and Ion Detection
Authors: Dharma Raj Pokhrel | Panmanas Sirisomboon | Lampan Khurnpoon | Jetsada Posom | Wanphut Saechua
Source: Sensors (article, 2023-06-04)
Field: Chemistry — Analytical Chemistry

Comparing Machine Learning and PLSDA Algorithms for Durian Pulp Classification Using Inline NIR Spectra The aim of this study was to evaluate and compare the performance of multivariate classification algorithms, specifically Partial Least Squares Discriminant Analysis (PLS-DA) and machine learning algorithms, in the classification of Monthong durian pulp based on its dry matter content (DMC) and soluble solid content (SSC), using the inline acquisition of near-infrared (NIR) spectra. A total of 415 durian pulp samples were collected and analyzed. Raw spectra were preprocessed using five different combinations of spectral preprocessing techniques: Moving Average with Standard Normal Variate (MA+SNV), Savitzky-Golay Smoothing with Standard Normal Variate (SG+SNV), Mean Normalization (SG+MN), Baseline Correction (SG+BC), and Multiplicative Scatter Correction (SG+MSC). The results revealed that the SG+SNV preprocessing technique produced the best performance with both the PLS-DA and machine learning algorithms. The optimized wide neural network algorithm of machine learning achieved the highest overall classification accuracy of 85.3%, outperforming the PLS-DA model, with overall classification accuracy of 81.4%. Additionally, evaluation metrics such as recall, precision, specificity, F1-score, AUC ROC, and kappa were calculated and compared between the two models. The findings of this study demonstrate the potential of machine learning algorithms to provide similar or better performance compared to PLS-DA in classifying Monthong durian pulp based on DMC and SSC using NIR spectroscopy, and they can be applied in the quality control and management of durian pulp production and storage. Artificial intelligence ; Linear discriminant analysis ; Algorithm ; Partial least squares regression ; Computer science ; Pulp (tooth) ; Mathematics ; Machine learning ; Preprocessor ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Seung‐Woo Nam | Youngjin Kim | Dongyeon Kim | Yoonchan Jeong
Source: ACM Transactions on Graphics (article, 2023-12-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Depolarized Holography with Polarization-Multiplexing Metasurface The evolution of computer-generated holography (CGH) algorithms has prompted significant improvements in the performances of holographic displays. Nonetheless, they start to encounter a limited degree of freedom in CGH optimization and physical constraints stemming from the coherent nature of holograms. To surpass the physical limitations, we consider polarization as a new degree of freedom by utilizing a novel optical platform called metasurface. Polarization-multiplexing metasurfaces enable incoherent-like behavior in holographic displays due to the mutual incoherence of orthogonal polarization states. We leverage this unique characteristic of a metasurface by integrating it into a holographic display and exploiting polarization diversity to bring an additional degree of freedom for CGH algorithms. To minimize the speckle noise while maximizing the image quality, we devise a fully differentiable optimization pipeline by taking into account the metasurface proxy model, thereby jointly optimizing spatial light modulator phase patterns and geometric parameters of metasurface nanostructures. We evaluate the metasurface-enabled depolarized holography through simulations and experiments, demonstrating its ability to reduce speckle noise and enhance image quality. Holography ; Multiplexing ; Computer science ; Polarization (electrochemistry) ; Optics ; Speckle pattern ; Orthogonal polarization spectral imaging ; Image quality ; Physics ; Computer vision ; Image (mathematics) ; Telecommunications ; Laser Metamaterials and Metasurfaces Applications ; Advanced Optical Imaging Technologies ; Advanced Antenna and Metasurface Technologies
Authors: Tjaša Rijavec | Matija Strlič | Irena Kralj Cigić
Source: Polymer Degradation and Stability (article, 2023-03-11)
Field: Materials Science — Polymers and Plastics

Damage function for poly(vinyl chloride) in heritage collections Yellowing of plastic objects as a consequence of chemical degradation is a common heritage conservation challenge. In the case of poly(vinyl chloride) elimination of hydrogen chloride leads to the formation of polyene sequences that act as chromophores. The objective of this work was to quantitatively evaluate the rate of degradation observed as yellowing, as relevant to room conditions during long-term storage of heritage collections. Degradation was quantified as increase in the b* colour coordinate during accelerated degradation at 50 and 70 °C as a function of temperature, relative humidity, plasticizer content, and polymer molecular weight. The significance of each variable was investigated with multiple linear regression. Lower temperature, lower relative humidity, higher polymer molecular weight and higher plasticizer content were associated with lower degradation rates. The activation energy of 86 kJ/mol was calculated. The concept of ‘1- °C-equivalent’ is introduced to enable variable prioritisation from a heritage management aspect. The resulting model can be used to shape environmental management guidelines and identify the most vulnerable objects in heritage collections. Relative humidity ; Vinyl chloride ; Polyene ; Plasticizer ; Degradation (telecommunications) ; Cultural heritage ; Polymer ; Chemical engineering ; Activation energy ; Materials science ; Chemistry ; Polymer chemistry ; Composite material ; Organic chemistry ; Thermodynamics ; Computer science ; Physics ; Geography ; Archaeology ; Engineering Polymer Science and PVC ; Cultural Heritage Materials Analysis ; Microplastics and Plastic Pollution
Authors: Maryam Asachi | Miller Alonso Camargo‐Valero
Source: Advanced Powder Technology (article, 2023-05-02)
Field: Chemistry — Analytical Chemistry

Multi-sensors data fusion for monitoring of powdered and granule products: Current status and future perspectives Process Analytical Technology (PAT) is a systematic approach for monitoring of process parameters and product quality attributes and nowadays is considered for continuous processing of many industrial products. It is a mechanism to design, analyse and control manufacturing processes through on-line, in-line, at-line and off-line configurations for monitoring Critical Quality Attributes (CQAs). PAT systems include a combination of reliable in-line sensors, spectroscopic instruments and Multivariate Statistical Methods (MSMs) to provide informative knowledge for quality assessment of powdered and granule products. Nevertheless, monitoring programs of advanced manufacturing processes based on PAT systems typically provide large sets of data which are complex to interpret. The application of appropriate data-driven modelling techniques could assist in the interpretation of complex data matrices to better control of processes. Data fusion is a data-driven approach that could increase performance and robustness of models used for data interpretation to generate more accurate knowledge about process conditions and performance by merging related outputs collected from several instruments and considering synergies from multiple sources. This paper aims at presenting the current state of the art regarding the application of multi-sensors data fusion for powdered and granule manufacturing processes and making a critical review of recent progress and future possible perspectives in this field. Process analytical technology ; Computer science ; Quality by Design ; Sensor fusion ; Critical quality attributes ; Statistical process control ; Data mining ; Granule (geology) ; Robustness (evolution) ; Process control ; Process engineering ; Data quality ; Process (computing) ; Biochemical engineering ; Systems engineering ; Reliability engineering ; Work in process ; New product development ; Engineering ; Artificial intelligence ; Materials science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fault Detection and Control Systems
Authors: Seongmin Park | Suk-Ju Hong | Sungjay Kim | Jiwon Ryu | Seung-Woo Roh | Ghiseok Kim
Source: Agriculture (article, 2023-04-21)
Field: Chemistry — Analytical Chemistry

Classification of Fresh and Frozen-Thawed Beef Using a Hyperspectral Imaging Sensor and Machine Learning The demand for safe and edible meat has led to the advancement of freeze-storage techniques, but falsely labeled thawed meat remains an issue. Many methods have been proposed for this purpose, but they all destroy the sample and can only be performed in the laboratory by skilled personnel. In this study, hyperspectral image data were used to construct a machine learning (ML) model to discriminate between freshly refrigerated, long-term refrigerated, and thawed beef meat samples. With four pre-processing methods, a total of five datasets were prepared to construct an ML model. The PLS-DA and SVM techniques were used to construct the models, and the performance was highest for the SVM model applying scatter correction and the RBF kernel function. These results suggest that it is possible to construct a prediction model to distinguish between fresh and non-fresh meat using the spectra obtained by purifying hyperspectral image data cubes, which can be a rapid and non-invasive method for routine analyses of the meat storage state. Hyperspectral imaging ; Support vector machine ; Construct (python library) ; Artificial intelligence ; Pattern recognition (psychology) ; Kernel (algebra) ; Computer science ; Mathematics ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Shuo Dong | Samuel Beaulieu | Malte Selig | Philipp Rosenzweig | Dominik Christiansen | Tommaso Pincelli | Maciej Dendzik | Jonas D. Ziegler | Julian Maklar | R. Patrick Xian | Alexander Neef | A. Mohammed | Armin Schulz | Mona Stadler | Michael Jetter | Peter Michler | Takashi Taniguchi | Kenji Watanabe | H. Takagi | Ulrich Starke | Alexey Chernikov | Martin Wolf | Hiro Nakamura | Andreas Knorr | Laurenz Rettig | Ralph Ernstorfer
Source: Nature Communications (article, 2023-08-19)
Field: Materials Science — Materials Chemistry

Observation of ultrafast interfacial Meitner-Auger energy transfer in a Van der Waals heterostructure Atomically thin layered van der Waals heterostructures feature exotic and emergent optoelectronic properties. With growing interest in these novel quantum materials, the microscopic understanding of fundamental interfacial coupling mechanisms is of capital importance. Here, using multidimensional photoemission spectroscopy, we provide a layer- and momentum-resolved view on ultrafast interlayer electron and energy transfer in a monolayer-WSe<sub>2</sub>/graphene heterostructure. Depending on the nature of the optically prepared state, we find the different dominating transfer mechanisms: while electron injection from graphene to WSe<sub>2</sub> is observed after photoexcitation of quasi-free hot carriers in the graphene layer, we establish an interfacial Meitner-Auger energy transfer process following the excitation of excitons in WSe<sub>2</sub>. By analysing the time-energy-momentum distributions of excited-state carriers with a rate-equation model, we distinguish these two types of interfacial dynamics and identify the ultrafast conversion of excitons in WSe<sub>2</sub> to valence band transitions in graphene. Microscopic calculations find interfacial dipole-monopole coupling underlying the Meitner-Auger energy transfer to dominate over conventional Förster- and Dexter-type interactions, in agreement with the experimental observations. The energy transfer mechanism revealed here might enable new hot-carrier-based device concepts with van der Waals heterostructures. Exciton ; Heterojunction ; Materials science ; Photoexcitation ; Graphene ; van der Waals force ; Excited state ; Chemical physics ; Electron transfer ; Atomic physics ; Condensed matter physics ; Nanotechnology ; Chemistry ; Physics ; Optoelectronics ; Physical chemistry ; Quantum mechanics 2D Materials and Applications ; Graphene research and applications ; Perovskite Materials and Applications
Authors: Nu Bich Duyen | Kristin Imenes | Knut E. Aasmundtveit | Hoang‐Vu Nguyen | Erik Andreassen
Source: Polymers (article, 2023-03-21)
Field: Materials Science — Materials Chemistry

Thermal Conductivity and Mechanical Properties of Polymer Composites with Hexagonal Boron Nitride—A Comparison of Three Processing Methods: Injection Moulding, Powder Bed Fusion and Casting Materials providing heat dissipation and electrical insulation are required for many electronic and medical devices. Polymer composites with hexagonal boron nitride (hBN) may fulfil such requirements. The focus of this study is to compare composites with hBN fabricated by injection moulding (IM), powder bed fusion (PBF) and casting. The specimens were characterised by measuring thermal conductivity, tensile properties, hardness and hBN particle orientation. A thermoplastic polyurethane (TPU) was selected as the matrix for IM and PBF, and an epoxy was the matrix for casting. The maximum filler weight fractions were 65%, 55% and 40% for IM, casting and PBF, respectively. The highest thermal conductivity (2.1 W/m∙K) was measured for an IM specimen with 65 wt% hBN. However, cast specimens had the highest thermal conductivity for a given hBN fraction. The orientation of hBN platelets in the specimens was characterised by X-ray diffraction and compared with numerical simulations. The measured thermal conductivities were discussed by comparing them with four models from the literature (the effective medium approximation model, the Ordóñez-Miranda model, the Sun model, and the Lewis-Nielsen model). These models predicted quite different thermal conductivities vs. filler fraction. Adding hBN increased the hardness and tensile modulus, and the tensile strength at high hBN fractions. The strength had a minimum as the function of filler fraction, while the strain at break decreased. These trends can be explained by two mechanisms which occur when adding hBN: reinforcement and embrittlement. Materials science ; Composite material ; Thermal conductivity ; Ultimate tensile strength ; Casting ; Injection moulding ; Epoxy ; Boron nitride Thermal properties of materials ; Tribology and Wear Analysis ; Advanced ceramic materials synthesis