jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiong Li | Yande Liu | Yunjuan Yan | Guantian Wang
Source: Spectroscopy (article, 2022-08-01)
Field: Chemistry — Analytical Chemistry

Detection of Early Bruises in Honey Peaches Using Shortwave Infrared Hyperspectral Imaging Honey peaches can bruise during harvesting, handling, storage, transportation, and distribution. In this study, the spectral range used was 400–1100 nm, and we extracted the RGB and HSI color space characteristics of the images. After principal component analysis (PCA) of the original data, the gray histogram features of the PC1 images were extracted. Partial least squares qualitative discriminant analysis (PLS-DA) and extreme learning machine (ELM) discriminant models were established. Among the 38 color features, the PLS-DA and ELM models had a high rate of misclassification, and the best classification accuracy was 74.29%. When extracting the spectral information of the bruised sample to build the model, the highest classification accuracy was 92.86% for the 176 characteristic wavelength points of the full band. In contrast, only 40 wavelength bands were used after selecting the genetic algorithm’s valid information. The classification accuracy of the PLS-DA model was 100%, which is because the softening and browning of the peach was not apparent after early bruising. However, the changes in the tissue’s thermal properties caused by internal defects are expressed in the internal spectrum. Therefore, the shortwave NIR hyperspectral imaging technique’s spectral information can detect the early bruising of peaches. Hyperspectral imaging ; Bruise ; Artificial intelligence ; Principal component analysis ; Pattern recognition (psychology) ; Linear discriminant analysis ; RGB color model ; YCbCr ; Computer science ; Mathematics ; Near-infrared spectroscopy ; Partial least squares regression ; Remote sensing ; Optics ; Image processing ; Color image ; Statistics ; Physics ; Image (mathematics) ; Geography Spectroscopy and Chemometric Analyses ; Bee Products Chemical Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Maimunah Mohd Ali | Norhashila Hashim | Samsuzana Abd Aziz | Ola Lasekan
Source: Research Square (preprint, 2022-04-08)
Field: Chemistry — Analytical Chemistry

Characterisation of pineapple cultivars under different storage conditions using infrared thermal imaging coupled with machine learning algorithms Abstract The non-invasive ability of infrared thermal imaging has gained interest in various food classification and recognition tasks. In this work, infrared thermal imaging was used to distinguish different pineapple cultivars i.e. MD2, Morris, and Josapine which were subjected at different storage temperatures i.e. 5, 10, and 25 °C and a relative humidity of 85 to 90 %. A total of 14 features from the thermal images were obtained to determine the variation in terms of image parameters among different pineapple cultivars. Principal component analysis was applied for feature reduction in order to prevent any effect of significant difference between the selected features. Several types of machine learning algorithms were compared including linear discriminant analysis, quadratic discriminant analysis, support vector machine, k-nearest neighbour, decision tree, and Naïve Bayes to obtain the best performance for the classification of pineapple cultivars. The results showed that support vector machine achieved the best performance from the combination of optimal image parameters with the highest classification rate of 100 %. The ability of infrared thermal imaging coupled with machine learning approaches can be potentially used to distinguish pineapple cultivars which could enhance the grading and sorting processes of the fruit. Artificial intelligence ; Support vector machine ; Machine learning ; Mathematics ; Linear discriminant analysis ; Principal component analysis ; Cultivar ; Algorithm ; Pattern recognition (psychology) ; Computer science ; Horticulture Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Advanced Chemical Sensor Technologies
Authors: Leqing Lin | Zhong Yu | Haoyang Lin | Chenglong Wang | Zhifei Yang | Qian Wu | Di Zhang | Wenguo Zhu | Yongchun Zhong | Yuwei Pan | Jianhui Yu | Huadan Zheng
Source: Molecules (article, 2022-07-05)
Field: Materials Science — Biomaterials

Spider Silk-Improved Quartz-Enhanced Conductance Spectroscopy for Medical Mask Humidity Sensing Spider silk is one of the hottest biomaterials researched currently, due to its excellent mechanical properties. This work reports a novel humidity sensing platform based on a spider silk-modified quartz tuning fork (SSM-QTF). Since spider silk is a kind of natural moisture-sensitive material, it does not demand additional sensitization. Quartz-enhanced conductance spectroscopy (QECS) was combined with the SSM-QTF to access humidity sensing sensitively. The results indicate that the resonance frequency of the SSM-QTF decreased monotonously with the ambient humidity. The detection sensitivity of the proposed SSM-QTF sensor was 12.7 ppm at 1 min. The SSM-QTF sensor showed good linearity of ~0.99. Using this sensor, we successfully measured the humidity of disposable medical masks for different periods of wearing time. The results showed that even a 20 min wearing time can lead to a >70% humidity in the mask enclosed space. It is suggested that a disposable medical mask should be changed <2 h. Humidity ; Spider silk ; Materials science ; SILK ; Relative humidity ; Quartz ; Conductance ; Moisture ; Spectroscopy ; Composite material ; Meteorology ; Mathematics ; Physics Silk-based biomaterials and applications ; Advanced Fiber Optic Sensors ; Neurobiology and Insect Physiology Research
Authors: Kevin M. Roccapriore | Nan Huang | Mark P. Oxley | Vinit Sharma | Timothy Taylor | Swagata Acharya | Dimitar Pashov | M. I. Katsnelson | David Mandrus | J. L. Musfeldt | Sergei V. Kalinin
Source: ACS Nano (article, 2022-09-29)
Field: Materials Science — Materials Chemistry

Electron-Beam Induced Emergence of Mesoscopic Ordering in Layered MnPS<sub>3</sub> Ordered mesoscale structures in 2D materials induced by small misorientations have allowed for a wide variety of electronic, ferroelectric, and quantum phenomena to be explored. Until now, the only mechanism to induce this periodic ordering was <i>via</i> mechanical rotations between the layers, with the periodicity of the resulting moiré pattern being directly related to twist angle. Here we report a fundamentally distinct mechanism for emergence of mesoscopic periodic patterns in multilayer sulfur-containing metal phosphorus trichalcogenide, MnPS<sub>3</sub>, induced by the electron beam. The formation under the beam of periodic hexagonal patterns with several characteristic length scales, nucleation and transitions between the phases, and local dynamics are demonstrated. The associated mechanisms are attributed to the relative contraction of the layers caused by beam-induced sulfur vacancy formation with subsequent ordering and lattice parameter change. As a result, the plasmonic response of the system is locally altered, suggesting an element of control over plasmon resonances by electron beam patterning. We pose that harnessing this phenomenon provides both insight into fundamental physics of quantum materials and enables device applications by enabling controlled periodic potentials on the atomic scale. Mesoscopic physics ; Materials science ; Cathode ray ; Condensed matter physics ; Nanotechnology ; Electron ; Chemical physics ; Physics ; Nuclear physics 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Semiconductor materials and interfaces
Authors: P. Boniecki | Agnieszka Sujak | Agnieszka A. Pilarska | H. Piekarska-Boniecka | Agnieszka Wawrzyniak | B. Raba
Source: Sensors (article, 2022-08-31)
Field: Chemistry — Analytical Chemistry

Dimension Reduction of Digital Image Descriptors in Neural Identification of Damaged Malting Barley Grains The paper covers the problem of determination of defects and contamination in malting barley grains. The analysis of the problem indicated that although several attempts have been made, there are still no effective methods of identification of the quality of barley grains, such as the use of information technology, including intelligent sensors (currently, quality assessment of grain is performed manually). The aim of the study was the construction of a reduced set of the most important graphic descriptors from machine-collected digital images, important in the process of neural evaluation of the quality of BOJOS variety malting barley. Grains were sorted into three size fractions and seed images were collected. As a large number of graphic descriptors implied difficulties in the development and operation of neural classifiers, a PCA (Principal Component Analysis) statistical method of reducing empirical data contained in the analyzed set was applied. The grain quality expressed by an optimal set of transformed descriptors was modelled using artificial neural networks (ANN). The input layer consisted of eight neurons with a linear Postsynaptic Function (PSP) and a linear activation function. The one hidden layer was composed of sigmoid neurons having a linear PSP function and a logistic activation function. One sigmoid neuron was the output of the network. The results obtained show that neural identification of digital images with application of Principal Component Analysis (PCA) combined with neural classification is an effective tool supporting the process of rapid and reliable quality assessment of BOJOS malting barley grains. Principal component analysis ; Artificial neural network ; Sigmoid function ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Dimensionality reduction ; Digital image ; Identification (biology) ; Mathematics ; Image processing ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Advanced Scientific Research Methods ; Surface Treatment and Coatings
Authors: Keming Liu | Xiaochun Sheng | Xiaolong Li | Mulin Li | Zhi Shen | Kai Fu | Haitao Zhou | Andrej Atrens
Source: Materials (article, 2022-04-22)
Field: Materials Science — Materials Chemistry

Deep Cryogenic Treatment Characteristics of a Deformation-Processed Cu-Ni-Co-Si Alloy This paper investigated the influence of deep cryogenic treatments (DCT) on the tensile strength, elongation to fracture and conductivity of a deformation-processed Cu-Ni-Co-Si alloy. The tensile properties were measured using a mechanical testing machine. The conductivity was evaluated using a low-resistance tester. The microstructure and precipitated phases were analyzed using scanning electron microscopy (SEM), transmission electron microscopy (TEM), an energy dispersive spectrometer (EDS) and an X-ray diffractometer (XRD). The tensile strength, elongation to fracture and conductivity of the Cu-1.34Ni-1.02Co-0.61Si alloy before and after cold rolling at 47% reduction increased with increasing DCT time and tended to be stable at about 36 h. The microstructure became more uniform after the DCT. The grain size was refined and was smallest after DCT for 48 h. The DCT promoted the precipitation of the solid solution elements Ni, Co and Si from the Cu matrix to form many fine and evenly distributed 20-70 nm spherical second-phase particles in the grains and grain boundaries. Alloy ; Materials science ; Cryogenic treatment ; Deformation (meteorology) ; Metallurgy ; Composite material ; Microstructure Metal Alloys Wear and Properties ; Advanced materials and composites ; Aluminum Alloys Composites Properties
Authors: Linda S. McDonald | Joe Panozzo
Source: Legume Science (review, 2022-12-08)
Field: Chemistry — Analytical Chemistry

A review of the opportunities for spectral‐based technologies in post‐harvest testing of pulse grains Abstract Pulse grains are phenotypically diverse varying in colour, size, shape, and uniformity and have been integrated within many cultures and cuisines for several thousand years. Consumption of pulses within traditional dishes is still the dominant use for these grains, and therefore, the marketability is largely based on visual characteristics. There is also increasing interest into the utilisation of pulses in new processed food products because of their high protein content. Pulse‐quality assessment is critical within industry to determine marketability of the produce and remuneration for growers; however, the methods for assessment are largely subjective, completed by visual appraisal. Furthermore, targeted pulse‐quality traits form part of the overall strategy of plant breeding programmes, but the grain‐assessment methodologies are time consuming, constraining testing efficiency, and some destructive tests are reserved for advanced germplasm. Recent advances in computing and spectral sensing technology have improved opportunities for development of non‐destructive, high‐throughput and accurate machine vision (MV) systems for product‐quality evaluation. Algorithms based on digital image analysis have been developed to classify and quantify characteristics relating to the size, shape, colour and defects of grains and other agricultural products. Additionally, near‐infrared‐spectral processing has been successfully applied in the prediction of compositional constituents, such as protein and moisture, for some agricultural products. This review describes the standard methodologies for the assessment of pulse‐quality traits and developments in MV applications for grain quality assessment. Opportunities are identified, both within the pulse grain industry and plant breeding programmes, for objective and standardised post‐harvest testing of pulse grains through MV. Quality (philosophy) ; Product (mathematics) ; Agricultural engineering ; Agriculture ; Throughput ; Remuneration ; Computer science ; Process engineering ; Environmental science ; Engineering ; Business ; Mathematics ; Geography ; Telecommunications Spectroscopy and Chemometric Analyses ; Food composition and properties ; Smart Agriculture and AI
Authors: Flávia P. Morais | Joana M. R. Curto
Source: Polymers (article, 2022-09-01)
Field: Materials Science — Polymers and Plastics

Design and Engineering of Natural Cellulose Fiber-Based Biomaterials with Eucalyptus Essential Oil Retention to Replace Non-Biodegradable Delivery Systems This work aims at the design and engineering of sustainable biomaterials based on natural fibers to replace non-renewable fiber sources in the development of non-woven delivery systems. Cellulose fibers were used as the main support to produce multi-structured materials with the incorporation of microfibrillated cellulose (MFC) as an additive. A 3D carboxymethylcellulose matrix retaining a natural bioactive product, eucalyptus essential oil, (CMC/EO), with controlled release functionalities, was also applied to these materials using bulk and spray coating methodologies. Additionally, using a 3D modeling and simulation strategy, different interest scenarios were predicted to design new formulations with improved functional properties. Overall, the results showed that MFC provided up to 5% improved strength (+48%) at the expense of reduced softness (−10%) and absorbency (−13%) and presented a good potential to be used as an additive to maximize natural eucalyptus fibers content in formulations. The addition of CMC/EO into formulations’ bulk revealed better strength properties (21–28%), while its surface coating improved absorption (23–25%). This indicated that both application methods can be used in structures proposed for different sustainable applications or a more localized therapy, respectively. This optimization methodology consists of a competitive benefit to produce high-quality functionalized biomaterials for added-value applications. Cellulose ; Natural fiber ; Fiber ; Materials science ; Tissue engineering ; Cellulose fiber ; Eucalyptus oil ; Biomedical engineering ; Chemical engineering ; Composite material ; Engineering ; Medicine Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Lignin and Wood Chemistry
Authors: Jack Thomas | Huajie Chen | Christoph Ortner
Source: Archive for Rational Mechanics and Analysis (article, 2022-08-06)
Field: Materials Science — Materials Chemistry

Body-Ordered Approximations of Atomic Properties We show that the local density of states (LDOS) of a wide class of tight-binding models has a weak body-order expansion. Specifically, we prove that the resulting body-order expansion for analytic observables such as the electron density or the energy has an exponential rate of convergence both at finite Fermi-temperature as well as for insulators at zero Fermi-temperature. We discuss potential consequences of this observation for modelling the potential energy landscape, as well as for solving the electronic structure problem. Physics ; Statistical physics ; Observable ; Fermi Gamma-ray Space Telescope ; Density of states ; Fermi gas ; Exponential function ; Electronic structure ; Convergence (economics) ; Electron ; Condensed matter physics ; Quantum mechanics ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Surface and Thin Film Phenomena
Authors: Lakhdar Sidi Salah | Nassira Ouslimani | Yann Danlée | Isabelle Huynen
Source: Micromachines (article, 2022-12-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Absorption Performances of PLA-Montmorillonite Nanocomposites Thin Films in Salisbury and Rozanov Configurations: Influence of Aging and Mechanical Recycling The present paper aims to address the crucial concern of pollution induced by growing plastic waste and electromagnetic interference (EMI). Nanocomposites combining poly(lactic acid) (PLA) and organo-modified montmorillonite (OMMT) are synthesized and compression molded into thin films. A first set of samples, referred as virgin, was kept as is, while a second set of samples were photochemically, thermally and hydrolytically aged before mechanical recycling via extruding and second compression molding, resulting in the so-called recycled composite. The electromagnetic (EM) properties with a focus on microwave absorption performances of virgin and recycled samples are compared for various thicknesses and weight concentrations of OMMT in PLA matrix. The EM performances are gauges by Rozanov and Salisbury structures that consist in one- and two-layer stacks of composite films back-coated by a metal foil. Characterization in Rozanov configuration shows an average absorption index over the Ka band of 29.3% and 21.1% for, respectively, virgin and recycled PLA reinforced with 4 wt.% OMMT. An optimization of the film thickness is proposed; up to 61.85% and 80% of absorption with a thickness of 1.4 mm and 3.75 mm, respectively, is reached with a metal back-coated rPLA-4%OMMT film. Characterization in Salisbury configuration gives advantage to the recycled structure with an average absorption of 49.6% for a total thickness of 1.4 mm. The requirements of EMI shielding are met by PLA-OMMT composites with a certain benefit of recycling process on EM performance. Materials science ; Composite material ; Montmorillonite ; Nanocomposite ; Absorption (acoustics) ; Compression molding ; Composite number ; Polycarbonate ; Molding (decorative) Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Natural Fiber Reinforced Composites
Authors: Benyamin Motevalli | Lachlan Hyde | Bronwyn Fox | Amanda S. Barnard
Source: Advanced Theory and Simulations (article, 2022-02-20)
Field: Materials Science — Materials Chemistry

Predicting the Probability of Observation of Arbitrary Graphene Oxide Nanoflakes Using Artificial Neural Networks Abstract Although it has been well established that the stability and properties of graphene oxide nanostructure are strongly influenced by the concentration, type, and distribution of oxygen groups on the surface, there has yet to be a definitive way of predicting the thermochemical stability in advance of detailed and time‐consuming experimentation or simulation. In this study, a data set of over 60 000 unique graphene oxide nanoflakes and supervised machine learning methods are used to predict the probability of observation (stability) with perfect accuracy, based on a limited set of structural features that can be controlled in advance. A decision tree is used to show how the features determine the stability, and a neural network provides an equation to predict the thermodynamic stability of virtually any configuration in minutes. This enables researchers to use machine learning as research planning tool or to assist in analyzing results from microanalysis. Graphene ; Stability (learning theory) ; Artificial neural network ; Oxide ; Decision tree ; Computer science ; Artificial intelligence ; Machine learning ; Nanostructure ; Set (abstract data type) ; Materials science ; Nanotechnology Graphene research and applications ; Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Tobias Eckert | Matthias Schmidt | Daniel de las Heras
Source: The Journal of Chemical Physics (article, 2022-12-15)
Field: Materials Science — Materials Chemistry

Sedimentation path theory for mass-polydisperse colloidal systems Both polydispersity and the presence of a gravitational field are inherent to essentially any colloidal experiment. While several theoretical works have focused on the effect of polydispersity on the bulk phase behavior of a colloidal system, little is known about the effect of a gravitational field on a polydisperse colloidal suspension. We extend here the sedimentation path theory to study sedimentation-diffusion-equilibrium of a mass-polydisperse colloidal system: the particles possess different buoyant masses but they are otherwise identical. The model helps to understand the interplay between gravity and polydispersity on sedimentation experiments. Since the theory can be applied to any parent distribution of buoyant masses, it can also be used to study the sedimentation of monodisperse colloidal systems. We find that mass-polydispersity has a strong influence in colloidal systems near density matching for which the bare density of the colloidal particles equals the solvent density. To illustrate the theory, we study crystallization in sedimentation-diffusion-equilibrium of a suspension of mass-polydisperse hard spheres. Dispersity ; Sedimentation ; Colloid ; Suspension (topology) ; Sedimentation equilibrium ; Gravitational field ; Materials science ; Chemical physics ; Diffusion ; Chemistry ; Thermodynamics ; Physics ; Classical mechanics ; Chromatography ; Geology ; Polymer chemistry ; Mathematics ; Physical chemistry ; Sediment Material Dynamics and Properties ; Phase Equilibria and Thermodynamics ; Advanced Thermodynamics and Statistical Mechanics