jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gang Tao | Qian Zhao | Qianju Song | Zao Yi | Yougen Yi | Qingdong Zeng
Source: Micromachines (article, 2024-11-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Polarization-Insensitive and Highly Sensitive THz Metamaterial Multi-Band Perfect Absorber In this article, we present a terahertz (THz) metamaterial absorber that blends two types of coordinated materials: Dirac semimetals and vanadium dioxide. Compared to other absorbers on the market, which are currently non-adjustable or have a single adjustment method, our absorber is superior because it has two coordinated modes with maximum adjustment ranges of 80.7% and 0.288 THz. The device contains four flawless absorption peaks (M1, M2, M3, and M4) spanning the frequency range of 2.0 THz to 6.0 THz, all with absorption rates greater than 99%. After calculation, the relative impedance of the device matches with that in free space, resulting in perfect absorption. In addition, our absorber has extremely excellent polarization insensitivity but is highly sensitive to changes in the environmental refractive index, with the highest environmental refractive index sensitivity of 716 GHz/RIU (gigahertz per refractive index unit). To sum up, the terahertz metamaterial absorber we showed has four perfect absorption peaks, high sensitivity, and stable polarization. This means it could be useful in areas like changing electromagnetic waves, making new sensors, and switching. Terahertz radiation ; Metamaterial absorber ; Metamaterial ; Refractive index ; Materials science ; Polarization (electrochemistry) ; Optoelectronics ; Optics ; Absorption (acoustics) ; Tunable metamaterials ; Physics ; Chemistry Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Joseph Deering | Valentin Nelea | Marc D. McKee
Source: Advanced Functional Materials (article, 2024-02-23)
Field: Materials Science — Biomaterials

Multiscale Mineralization in the Leopard Gecko Eggshell Abstract Reptilian (non‐avian) eggshell biomineralization is markedly different from avian eggshells. Despite discontinuous/variable mineralization, the reptilian eggshell (like in birds) is responsible for protective and developmental functions of the egg as part of a highly successful reproduction strategy. Using multiscale imaging and characterization methods including X‐ray and electron microscopy/tomography, a 3D overview of eggshell ultrastructure in the gekkotan Eublepharis macularius – the common leopard gecko is provided. Eggshell mineral is shown to be sensitive to aqueous preparations, where cryogenic methods are essential for accurate mineral and organic preservation. Interwoven with the mineral are underlying organic fibers involved in the formation of complex hierarchical order in the eggshell, including nucleation and growth of mineral. The calcitic mineral phase of the eggshell takes on three microstructural motifs at the shell surface: nodular scutes (≈40 µm), intermittent blunt or star‐shaped mineral rosettes (≈1 mm), and smaller mineral corollas (≈10 µm). Individual scutes have two hierarchical mineral structures – mineral nanospheres (≈500 nm) and mineral nanodomains (≈40 nm). Diverse and variable biomineralization features of reptilian eggshells likely reflect the importance of differences in ecology and physiology on egg formation, survival, and eggshell mechanics, and are likely related to hydration sensitivity, structural organic‐inorganic interactions, and mineral polymorphism. Gecko ; Materials science ; Eggshell ; Nanotechnology ; Ecology ; Biology Calcium Carbonate Crystallization and Inhibition ; Physiological and biochemical adaptations ; Paleontology and Evolutionary Biology
Authors: Xuepeng Zheng | Bin Nie | Jianqiang Du | Yi Rao | Huan Li | Jiandong Chen | Yuwen Du | Yuchao Zhang | Haike Jin
Source: Frontiers in Physiology (article, 2024-05-01)
Field: Chemistry — Analytical Chemistry

A non-linear partial least squares based on monotonic inner relation A novel regression model, monotonic inner relation-based non-linear partial least squares (MIR-PLS), is proposed to address complex issues like limited observations, multicollinearity, and nonlinearity in Chinese Medicine (CM) dose-effect relationship experimental data. MIR-PLS uses a piecewise mapping function based on monotonic cubic splines to model the non-linear inner relations between input and output score vectors. Additionally, a new weight updating strategy (WUS) is developed by leveraging the properties of monotonic functions. The proposed MIR-PLS method was compared with five well-known PLS variants: standard PLS, quadratic PLS (QPLS), error-based QPLS (EB-QPLS), neural network PLS (NNPLS), and spline PLS (SPL-PLS), using CM dose-effect relationship datasets and near-infrared (NIR) spectroscopy datasets. Experimental results demonstrate that MIR-PLS exhibits general applicability, achieving excellent predictive performances in the presence or absence of significant non-linear relationships. Furthermore, the model is not limited to CM dose-effect relationship research and can be applied to other regression tasks. Partial least squares regression ; Monotonic function ; Multicollinearity ; Linear regression ; Mathematics ; Piecewise ; Linear model ; Regression ; Relation (database) ; Piecewise linear function ; Computer science ; Applied mathematics ; Algorithm ; Statistics ; Data mining Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Matthew E.K. Chang | Jane Lange | Jessie May Cartier | Travis W. Moore | Shannon Kimberly P. Soriano | Brenna Albracht | Michael Krawitzky | Harendra Guturu | Amir H. Alavi | Alexey Stukalov | Xiaoyuan Zhou | Eltaher M. Elgierari | Jessica Chu | Ryan W. Benz | Juan C. Cuevas | Shadi Ferdosi | Daniel Hornburg | Omid C. Farokhzad | Asim Siddiqui | Serafim Batzoglou | Robin J. Leach | Michael A. Liss | Ryan Kopp | Mark R. Flory
Source: International Journal of Molecular Sciences (article, 2024-07-23)
Field: Chemistry — Spectroscopy

A Scaled Proteomic Discovery Study for Prostate Cancer Diagnostic Markers Using ProteographTM and Trapped Ion Mobility Mass Spectrometry There is a significant unmet need for clinical reflex tests that increase the specificity of prostate-specific antigen blood testing, the longstanding but imperfect tool for prostate cancer diagnosis. Towards this endpoint, we present the results from a discovery study that identifies new prostate-specific antigen reflex markers in a large-scale patient serum cohort using differentiating technologies for deep proteomic interrogation. We detect known prostate cancer blood markers as well as novel candidates. Through bioinformatic pathway enrichment and network analysis, we reveal associations of differentially abundant proteins with cytoskeletal, metabolic, and ribosomal activities, all of which have been previously associated with prostate cancer progression. Additionally, optimized machine learning classifier analysis reveals proteomic signatures capable of detecting the disease prior to biopsy, performing on par with an accepted clinical risk calculator benchmark. Prostate cancer ; Prostate ; Medicine ; Proteomics ; Prostate-specific antigen ; Oncology ; Cancer ; Biomarker discovery ; Bioinformatics ; Computational biology ; Internal medicine ; Biology ; Gene Advanced Proteomics Techniques and Applications ; Prostate Cancer Treatment and Research ; Mass Spectrometry Techniques and Applications
Authors: Wenhao Yuan | Bingqing Yao | Shengdong Tan | Qian He
Source: Microscopy and Microanalysis (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Generative AI Enables Label-Free Segmentation for Live Analysis of Supported Nanoparticle Catalysts Segmentation ; Generative grammar ; Nanoparticle ; Materials science ; Computer science ; Nanotechnology ; Catalysis ; Artificial intelligence ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: José Ordoñez-Miranda | Roman Anufriev | Masahiro Nomura | Sébastian Volz
Source: Physical Review Applied (article, 2024-05-20)
Field: Materials Science — Materials Chemistry

Harnessing thermal waves for heat pumping Based on the nonlinear propagation of thermal diffusion waves, we demonstrate the existence of a net heat current even in the absence of a mean temperature gradient. Unlike the steady-state heat current, this current of thermal waves is not driven by the material thermal conductivity, but rather by its temperature derivative. The heat current propagates outward or inward of its excitation source when the thermal conductivity increases or decreases with temperature, respectively. The modulation of the heat flux direction can thus be achieved by varying the material temperature around the thermal conductivity peak exhibited by various dielectrics. For a silicon plate with a thermal conductivity peak at 40 K that supports the propagation of thermal waves with an excitation amplitude of 10 K, we observe a net heat flux exceeding $60\phantom{\rule{0.1em}{0ex}}\text{W}\phantom{\rule{0.1em}{0ex}}{\mathrm{cm}}^{\ensuremath{-}2}$ at 20 K and falling below $\ensuremath{-}20$ W ${\mathrm{cm}}^{\ensuremath{-}2}$ at 65 K. Larger excitation amplitudes allow higher or lower heat fluxes to be achieved, which makes them easy to observe and potentially apply for harvesting or evacuating thermal energy in systems undergoing natural temperature fluctuations on earth and in outer space. Thermal conductivity ; Heat flux ; Excitation ; Materials science ; Amplitude ; Thermal ; Thermal conduction ; Thermodynamics ; Condensed matter physics ; Physics ; Heat transfer ; Optics ; Composite material Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Advanced Thermoelectric Materials and Devices
Authors: Hamid Reza Ahmadi | Zaher Rahimi | Wojciech Sumelka
Source: Applied Sciences (article, 2024-04-03)
Field: Materials Science — Materials Chemistry

Enhancing Sensitivity of Double-Walled Carbon Nanotubes with Longitudinal Magnetic Field In this study, the behavior of double-walled carbon nanotubes (DWCNTs) used as mass sensors is explored under various boundary conditions; particular attention is paid to the crucial topic of resonant nanomechanical mass sensors. In the presented approach, nanotubes are subjected to a distributed transverse magnetic force and supported by an elastic foundation. The impacts of the longitudinal magnetic field, elastic medium, and diverse physical parameters on the responsiveness of the sensors are assessed. Using the energy method, governing equations are formulated to determine the frequency shifts of the mass nanosensors. Our findings reveal significant variations in the frequency shifts due to a longitudinal magnetic field, which depends on the applied boundary conditions. This research holds significance in the design of resonant nanomechanical mass sensors and provides valuable insights into the interplay of factors affecting their performance. Through exploring the intricate dynamics of DWCNTs used as mass sensors and thus contributing to the broader understanding of nanoscale systems, the implications for advancements in sensor design are offered and applications are introduced. Carbon nanotube ; Materials science ; Sensitivity (control systems) ; Longitudinal field ; Magnetic field ; Composite material ; Nanotechnology ; Physics ; Engineering ; Electronic engineering Carbon Nanotubes in Composites ; Mechanical and Optical Resonators ; Nonlocal and gradient elasticity in micro/nano structures
Authors: Millatul Ulya | Nur Chamidah | Toha Saifudin
Source: F1000Research (preprint, 2024-03-18)
Field: Chemistry — Analytical Chemistry

Mango quality prediction based on near-infrared spectroscopy using multi-predictor local polynomial regression modeling <ns3:p>Background pH and total soluble solids (TSS) are important quality parameters of mangoes; they represent the acidity and sweetness of the fruit, respectively. This study predicts the pH and TSS of intact mangoes based on near-infrared (NIR) spectroscopy using multi-predictor local polynomial regression (MLPR) modeling. Herein, the prediction performance of kernel partial least square regression (KPLSR), support vector machine regression (SVMR), and MLPR is compared. Methods For this purpose, 186 intact mango samples at three different maturity stages are used. Prediction models are built using MLPR, KPLSR, and SVMR based on untreated and treated spectra. The best regression model for predicting pH is MLPR based on Gaussian filter smoothing spectra. Moreover, the TSS value is more accurately predicted using MLPR based on Savitzky–Golay smoothing. Results The findings reveal that MLPR is highly accurate in estimating the pH and TSS of mangoes, with mean absolute percentage error (MAPE) values less than 10 %. In addition, the MLPR model has the best predictive performance with the lowest Mean Squared error (MSE) and root mean squared error (RMSE) values and the highest R2 value. Conclusions The use of NIR spectroscopy in combination with multi-predictor local polynomial regression could provide a quick and non-destructive technique for predicting mango quality. Thus, the results of this study help support sustainable production as a sustainable development goal.</ns3:p> Open peer review ; Plant biology ; Polynomial regression ; Regression analysis ; Quality (philosophy) ; Regression ; Polynomial ; Medicine ; Statistics ; Mathematics ; Biology ; Botany ; Physics ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing in Agriculture
Authors: Alberto Guerra | Marco Franzoi | V. Vigolo | Enrico Tosetto | Massimo De Marchi
Source: ACTA IMEKO (article, 2024-03-22)
Field: Chemistry — Analytical Chemistry

Detection of common adulterants in bulk bovine milk using fourier transformed mid-infrared spectroscopy Fourier transformed mid-infrared spectroscopy is a widespread method for routine analysis in milk. This method can also be used for the detection of adulterants in bovine milk which represent a current risk for dairy industries and public health. This work focuses on the detection of seven adulterants present in three known concentrations in bulk milk sample. The adulterants were: sodium bicarbonate, sodium chloride, hydroxyproline, glucose, sodium citrate, water, and urea. Partial least squares – discriminant analysis was used to develop statistical models to predict the presence of adulterants in milk samples. The obtained models could provide an easy, efficient, and rapid tool for the dairy industry to detect specific adulterants in milk. Spectroscopy ; Infrared ; Infrared spectroscopy ; Fourier transform infrared spectroscopy ; Fourier transform ; Materials science ; Analytical Chemistry (journal) ; Fourier transform spectroscopy ; Chemistry ; Mathematics ; Optics ; Physics ; Chromatography ; Organic chemistry ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Meat and Animal Product Quality
Authors: Wu Wei Zhou | Fu Ju Ye | Xiao Qi Li | Hao Yang Cui | Lei Chen
Source: Journal of Applied Physics (article, 2024-01-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Intelligent programmable metasurface for vibration field sensing and electromagnetic reflection modulation While existing research has explored control strategies in multi-dimensional fields, the interconnectedness between electromagnetic and vibrational fields remains relatively uncharted. To explore this intersection and harness its potential, we propose an intelligent programmable metasurface that modulates electromagnetic fields based on vibration intensity information. This paper introduces a groundbreaking approach that synergizes the physical fields of mechanical (vibrational) waves with electromagnetic waves, facilitating the detection and manipulation of information from both fields. By combining a programmable metasurface, vibration sensors, and microcontroller units, we have achieved regulation of the electromagnetic field through utilization of vibration intensity information. In this work, we have introduced six coding patterns that facilitate dual-beam scanning with variable deflection angles. A 20 × 20 metasurface is fabricated and measured, and the measured results are in good agreement with the simulated results. This research opens a new avenue for manipulating electromagnetic waves. Furthermore, the findings have the potential to impact a wide range of fields, including building structuralhealth monitoring, industrial production, mechanical equipment monitoring, and earthquake monitoring. Electromagnetic radiation ; Vibration ; Electromagnetic field ; Acoustics ; Deflection (physics) ; Computer science ; Physics ; Optics ; Electronic engineering ; Engineering Metamaterials and Metasurfaces Applications ; Indoor and Outdoor Localization Technologies ; Speech and Audio Processing
Authors: Zahra Elhamraoui | Eva Borràs | Mathias Wilhelm | Eduard Sabidó
Source: Analytical Chemistry (article, 2024-09-25)
Field: Chemistry — Spectroscopy

Theoretical Assessment of Indistinguishable Peptides in Mass Spectrometry-Based Proteomics Mass-spectrometry-based proteomics has advanced with the integration of experimental and predicted spectral libraries, which have significantly improved peptide identification in complex search spaces. However, challenges persist in distinguishing some peptides with close retention times and nearly identical fragmentation patterns. In this study, we conducted a theoretical assessment to quantify the prevalence of indistinguishable peptides within the human canonical proteome and immunopeptidome using state-of-the-art retention time and spectrum prediction models. By quantifying the proportion of peptides posing challenges to unequivocal identification, we set the theoretical nonaccessible portion within a given proteome, and underscore the effectiveness of contemporary analytical methodologies in resolving the complexity of the human proteome and immunopeptidome via mass spectrometry. Chemistry ; Mass spectrometry ; Proteomics ; Chromatography ; Isobaric labeling ; Computational biology ; Protein mass spectrometry ; Tandem mass spectrometry ; Biochemistry Advanced Proteomics Techniques and Applications ; vaccines and immunoinformatics approaches ; Machine Learning in Bioinformatics
Authors: Chao‐Hui Feng | Hirofumi Arai | Francisco J. Rodríguez‐Pulido
Source: Life (article, 2024-05-20)
Field: Chemistry — Analytical Chemistry

Evaluating Moisture Content in Immersion Vacuum-Cooled Sausages with Citrus Peel Extracts Using Hyperspectral Imaging The moisture content of immersion vacuum-cooled sausages with modified casings containing citrus fruit extracts under different storage conditions was studied using hyperspectral imaging (HSI) associated with chemometrics. Different pre-processing combinations were applied to improve the robustness of the model. The partial least squares regression model, employing the full reflectance spectrum with pre-treatment of the standard normal variate, showed calibration coefficients of determination (R<sub>c</sub><sup>2</sup>) of 0.6160 and a root mean square error of calibration (RMSEC) of 2.8130%. For the first time, prediction maps developed via HSI visualized the distribution of moisture content in the immersion vacuum-cooled sausages with unique modified casings in response to fluctuating storage conditions. The prediction maps showed exact parts with high water content, which will help us to monitor and prevent mold growth. The combination of HSI with multivariate analysis not only quantifies changes in moisture content but also visually represents them in response to various casing treatments under different storage conditions, illustrating the significant potential for real-time inspection and early mold detection in sausages within the processed meat industry. Hyperspectral imaging ; Water content ; Immersion (mathematics) ; Moisture ; Food science ; Materials science ; Pulp and paper industry ; Chemistry ; Composite material ; Mathematics ; Remote sensing ; Engineering ; Geology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Phytochemicals and Antioxidant Activities
Authors: Runzhe Zhang | Siyuan Qiao | Yixiong Luo | Yinghui Guo | Xiaoyin Li | Qi Zhang | Yulong Fan | Zeyu Zhao | Xiangang Luo
Source: Nanomaterials (article, 2024-05-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Structured-Light 3D Imaging Based on Vector Iterative Fourier Transform Algorithm Quasi-continuous-phase metasurfaces overcome the side effects imposed by high-order diffraction on imaging and can impart optical parameters such as amplitude, phase, polarization, and frequency to incident light at sub-wavelength scales with high efficiency. Structured-light three-dimensional (3D) imaging is a hot topic in the field of 3D imaging because of its advantages of low computation cost, high imaging accuracy, fast imaging speed, and cost-effectiveness. Structured-light 3D imaging requires uniform diffractive optical elements (DOEs), which could be realized by quasi-continuous-phase metasurfaces. In this paper, we design a quasi-continuous-phase metasurface beam splitter through a vector iterative Fourier transform algorithm and utilize this device to realize structured-light 3D imaging of a target object with subsequent target reconstruction. A structured-light 3D imaging system is then experimentally implemented by combining the fabricated quasi-continuous-phase metasurface illuminated by the vertical-cavity surface-emitting laser and a binocular recognition system, which eventually provides a new technological path for the 3D imaging field. Optics ; Fourier transform ; Beam splitter ; Computation ; Computer science ; Light field ; Structured light ; Polarization (electrochemistry) ; Materials science ; Algorithm ; Physics ; Laser Metamaterials and Metasurfaces Applications ; Advanced Optical Imaging Technologies ; Orbital Angular Momentum in Optics
Authors: Chase L. S. Skawinski | Priya S. Shah
Source: Journal of Proteome Research (review, 2024-04-01)
Field: Chemistry — Spectroscopy

I’m Walking into Spiderwebs: Making Sense of Protein–Protein Interaction Data Protein-protein interactions (PPIs) are at the heart of the molecular landscape permeating life. Proteomics studies can explore this protein interaction landscape using mass spectrometry (MS). Thanks to their high sensitivity, mass spectrometers can easily identify thousands of proteins within a single sample, but that same sensitivity generates tangled spiderwebs of data that hide biologically relevant findings. So, what does a researcher do when she finds herself walking into spiderwebs? In a field focused on discovery, MS data require rigor in their analysis, experimental validation, or a combination of both. In this Review, we provide a brief primer on MS-based experimental methods to identify PPIs. We discuss approaches to analyze the resulting data and remove the proteomic background. We consider the advantages between comprehensive and targeted studies. We also discuss how scoring might be improved through AI-based protein structure information. Women have been essential to the development of proteomics, so we will specifically highlight work by women that has made this field thrive in recent years. Proteomics ; Computer science ; Data science ; Field (mathematics) ; Computational biology ; Protein–protein interaction ; Mass spectrometry ; Bioinformatics ; Chemistry ; Biology ; Chromatography ; Biochemistry Advanced Proteomics Techniques and Applications ; Biotin and Related Studies ; Bioinformatics and Genomic Networks