jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: P.K. Praseetha | R. I. Jari Litany | Hanan M. Alharbi | Alaa A. Khojah | Shopnil Akash | Mohammed Bourhia | Atrsaw Asrat Mengistie | Gamal A. Shazly
Source: Scientific Reports (article, 2024-10-18)
Field: Materials Science — Materials Chemistry

Green synthesis of highly fluorescent carbon quantum dots from almond resin for advanced theranostics in biomedical applications Fluorescent Carbon Quantum Dots (CQDs) are being used in medical applications, particularly in theranostics. These Carbon Quantum Dots have been gaining more attention lately due to their potential as an effective replacement for hazardous synthetic organic dyes in a variety of biomedical applications, including live cell imaging and diagnostics. In this study, highly fluorescent Carbon Quantum Dots by one pot microwave based green route with a size of less than 10 nm, was prepared from commercially available almond resin, Prunus dulcis and conjugated with honey as additional reagent for surface functionalization. They exhibit a deep blue emission on excitation at 350 nm with an elevated quantum yield at 61%. They possess atomic nature and basic features such as high photo-stability, varying fluorescence, greater biocompatibility, and better water solubility. These fluorescent labels exhibit faster cellular invagination without disturbing the cell stability. The CQDs present cell imaging capacity with multi-coloration for visualizing the fine architecture of the nucleus naming, the nuclear membrane and nucleolus, which is linked with their varied, surface structures such as amphiphilic property and higher positive charges. These characteristics with minimal invasion have made carbon quantum dots to become the spotlight in theranostics. They can be used as alternatives to synthetic dyes for fluorescence- related cell-imaging. The intriguing fact about this approach is that it opens the possibility of combining therapy and diagnostics into one unit, which can alter how some diseases are handled and, in turn, transform the field of healthcare. Carbon quantum dots ; Fluorescence ; Quantum dot ; Nanotechnology ; Carbon fibers ; Materials science ; Chemistry ; Composite material ; Physics Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Graphene and Nanomaterials Applications
Authors: S.Yu. Kovtareva | Л. Кусепова | Gaukhar Tazhkenova | Togzhan Mashan | Karlygash Zh. Bazarbaeva | Eldar Kopishev
Source: Polymers (article, 2024-04-16)
Field: Materials Science — Biomaterials

Surface Modification of Mesoporous Silica Nanoparticles for Application in Targeted Delivery Systems of Antitumour Drugs The problem of tumour therapy has attracted the attention of many researchers for many decades. One of the promising strategies for the development of new dosage forms to improve oncology treatment efficacy and minimise side effects is the development of nanoparticle-based targeted transport systems for anticancer drugs. Among inorganic nanoparticles, mesoporous silica deserves special attention due to its outstanding surface properties and drug-loading capability. This review analyses the various factors affecting the cytotoxicity, cellular uptake, and biocompatibility of mesoporous silica nanoparticles (MSNs), constituting a key aspect in the development of safe and effective drug delivery systems. Special attention is paid to technological approaches to chemically modifying MSNs to alter their surface properties. The stimuli that regulate drug release from nanoparticles are also discussed, contributing to the effective control of the delivery process in the body. The findings emphasise the importance of modifying MSNs with different surface functional groups, bio-recognisable molecules, and polymers for their potential use in anticancer drug delivery systems. Mesoporous silica ; Nanotechnology ; Drug delivery ; Biocompatibility ; Nanoparticle ; Targeted drug delivery ; Drug ; Surface modification ; Materials science ; Controlled release ; Mesoporous material ; Drug carrier ; Chemistry ; Pharmacology ; Medicine ; Organic chemistry Nanoparticle-Based Drug Delivery ; Mesoporous Materials and Catalysis ; Dendrimers and Hyperbranched Polymers
Authors: Daniel Arenas Esteban | Da Wang | Ajinkya Kadu | Noa Olluyn | Ana Sánchez‐Iglesias | Alejandro Gómez Pérez | J. González-Casablanca | Stavros Nicolopoulos | Luis M. Liz‐Marzán | Sara Bals
Source: Nature Communications (article, 2024-07-30)
Field: Materials Science — Materials Chemistry

Quantitative 3D structural analysis of small colloidal assemblies under native conditions by liquid-cell fast electron tomography Electron tomography has become a commonly used tool to investigate the three-dimensional (3D) structure of nanomaterials, including colloidal nanoparticle assemblies. However, electron microscopy is typically done under high-vacuum conditions, requiring sample preparation for assemblies obtained by wet colloid chemistry methods. This involves solvent evaporation and deposition on a solid support, which consistently alters the nanoparticle organization. Here, we suggest using electron tomography to study nanoparticle assemblies in their original colloidal liquid environment. To address the challenges related to electron tomography in liquid, we devise a method that combines fast data acquisition in a commercial liquid-cell with a dedicated alignment and reconstruction workflow. We present the advantages of this methodology in accurately characterizing two different systems. 3D reconstructions of assemblies comprising polystyrene-capped Au nanoparticles encapsulated in polymeric shells reveal less compact and more distorted configurations for experiments performed in a liquid medium compared to their dried counterparts. A similar expansion can be observed in quantitative analysis of the surface-to-surface distances of self-assembled Au nanorods in water rather than in a vacuum, in agreement with bulk measurements. This study, therefore, emphasizes the importance of developing high-resolution characterization tools that preserve the native environment of colloidal nanostructures. Electron tomography ; Characterization (materials science) ; Materials science ; Colloid ; Nanotechnology ; Nanoparticle ; Polystyrene ; Evaporation ; Cryo-electron tomography ; Resolution (logic) ; Nanorod ; Tomography ; Nanostructure ; Nanomaterials ; Chemical physics ; Chemical engineering ; Chemistry ; Transmission electron microscopy ; Scanning transmission electron microscopy ; Composite material ; Computer science ; Optics ; Polymer ; Physics Pickering emulsions and particle stabilization ; Advanced Electron Microscopy Techniques and Applications ; Characterization and Applications of Magnetic Nanoparticles
Authors: Chenlong Fan | Ying Liu | Tao Cui | Mengmeng Qiao | Yu Yang | Weijun Xie | Yuping Huang
Source: Foods (article, 2024-12-23)
Field: Chemistry — Analytical Chemistry

Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy Rapid and accurate detection of protein content is essential for ensuring the quality of maize. Near-infrared spectroscopy (NIR) technology faces limitations due to surface effects and sample homogeneity issues when measuring the protein content of whole maize grains. Focusing on maize grain powder can significantly improve the quality of data and the accuracy of model predictions. This study aims to explore a rapid detection method for protein content in maize grain powder based on near-infrared spectroscopy. A method for determining protein content in maize grain powder was established using near-infrared (NIR) reflectance spectra in the 940-1660 nm range. Various preprocessing techniques, including Savitzky-Golay (S-G), multiplicative scatter correction (MSC), standard normal variate (SNV), and the first derivative (1D), were employed to preprocess the raw spectral data. Near-infrared spectral data from different varieties of maize grain powder were collected, and quantitative analysis of protein content was conducted using Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Extreme Learning Machine (ELM) models. Feature wavelengths were selected to enhance model accuracy further using the Successive Projections Algorithm (SPA) and Uninformative Variable Elimination (UVE). Experimental results indicated that the PLSR model, preprocessed with 1D + MSC, yielded the best performance, achieving a root mean square error of prediction (RMSEP) of 0.3 g/kg, a correlation coefficient (R<sub>p</sub>) of 0.93, and a residual predictive deviation (RPD) of 3. The associated methods and theoretical foundation provide a scientific basis for the quality control and processing of maize. Partial least squares regression ; Near-infrared spectroscopy ; Mean squared error ; Biological system ; Residual ; Mathematics ; Correlation coefficient ; Second derivative ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Statistics ; Algorithm ; Physics ; Optics ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Ella M. King | Chrisy Xiyu Du | Qian-Ze Zhu | Samuel S. Schoenholz | Michael P. Brenner
Source: Proceedings of the National Academy of Sciences (article, 2024-06-24)
Field: Materials Science — Materials Chemistry

Programming patchy particles for materials assembly design Direct design of complex functional materials would revolutionize technologies ranging from printable organs to novel clean energy devices. However, even incremental steps toward designing functional materials have proven challenging. If the material is constructed from highly complex components, the design space of materials properties rapidly becomes too computationally expensive to search. On the other hand, very simple components such as uniform spherical particles are not powerful enough to capture rich functional behavior. Here, we introduce a differentiable materials design model with components that are simple enough to design yet powerful enough to capture complex materials properties: rigid bodies composed of spherical particles with directional interactions (patchy particles). We showcase the method with self-assembly designs ranging from open lattices to self-limiting clusters, all of which are notoriously challenging design goals to achieve using purely isotropic particles. By directly optimizing over the location and interaction of the patches on patchy particles using gradient descent, we dramatically reduce the computation time for finding the optimal building blocks. Ranging ; Computer science ; Simple (philosophy) ; Computation ; Limiting ; Isotropy ; Nanotechnology ; Material Design ; Biological system ; Computational science ; Materials science ; Physics ; Mechanical engineering ; Algorithm ; Engineering Pickering emulsions and particle stabilization ; Advanced Materials and Mechanics ; Micro and Nano Robotics
Authors: Muntaha Nasir | Farhan Javaid | Mehar Masood | Muhammad Arshad | Muhammad Yasir | Vladimír Sedlařík | Muhammad Abdel Qadir | Hazim Qiblawey | Wenjuan Zhang | Kashif Mairaj Deen | Edouard Asselin | Nasir M. Ahmad
Source: Environmental Science Advances (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Regenerable chitosan-embedded magnetic iron oxide beads for nitrate removal from industrial wastewater Synthesis of regenerable chitosan-embedded magnetic iron oxide beads. Chitosan ; Iron oxide ; Oxide ; Industrial wastewater treatment ; Nitrate ; Wastewater ; Chemistry ; Chemical engineering ; Materials science ; Waste management ; Organic chemistry ; Engineering Nanomaterials for catalytic reactions ; Phosphorus and nutrient management ; Adsorption and biosorption for pollutant removal
Authors: Jiarui Feng | Shenghui Zhang | Zhaoyu Zhai | Hongfeng Yu | Huanliang Xu
Source: Plant Phenomics (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

DC2Net: An Asian Soybean Rust Detection Model Based on Hyperspectral Imaging and Deep Learning Asian soybean rust (ASR) is one of the major diseases that causes serious yield loss worldwide, even up to 80%. Early and accurate detection of ASR is critical to reduce economic losses. Hyperspectral imaging, combined with deep learning, has already been proved as a powerful tool to detect crop diseases. However, current deep learning models are limited to extract both spatial and spectral features in hyperspectral images due to the use of fixed geometric structure of the convolutional kernels, leading to the fact that the detection accuracy of current models remains further improvement. In this study, we proposed a deformable convolution and dilated convolution neural network (DC<sup>2</sup>Net) for the ASR detection. The deformable convolution module was used to extract the spatial features, while the dilated convolution module was applied to extract features from the spectral dimension. We also adopted the Shapley value and the channel attention methods to evaluate the importance of each wavelength during decision-making, thereby identifying the most contributing ones. The proposed DC<sup>2</sup>Net can realize early asymptomatic detection of ASR even when visual symptoms have not appeared. The results of the experiment showed that the detection performance of DC<sup>2</sup>Net dominated state-of-the-art methods, reaching an overall accuracy at 96.73%. Meanwhile, the experimental result suggested that the Shapley Additive exPlanations method was able to extract feature wavelengths correctly, thereby helping DC<sup>2</sup>Net achieve reasonable performance with less input data. The research result of this study could provide early warning of ASR outbreak in advance, even at the asymptomatic period. Hyperspectral imaging ; Convolution (computer science) ; Computer science ; Artificial intelligence ; Convolutional neural network ; Deep learning ; Pattern recognition (psychology) ; Rust (programming language) ; Artificial neural network Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Plant Pathogens and Fungal Diseases
Authors: Ayşenur Gürgen | Мustafa Sevindik | Tetiana Krupodorova | İmran Uysal | Orhan Ünal
Source: BMC Biotechnology (article, 2024-10-29)
Field: Chemistry — Analytical Chemistry

Biological activities of Hypericum spectabile extract optimized using artificial neural network combined with genetic algorithm application Optimizing extraction conditions can help maximize the efficiency and yield of the extraction process while minimizing negative impacts on the environment and human health. For the purpose of the current study, an artificial neural network (ANN) combined with a genetic algorithm (GA) was utilized for that the extraction conditions of Hypericum spectabile were optimized. In this particular investigation, the main objective was to get the highest possible levels of total antioxidant status (TAS) for the extracts that were obtained. In addition to this, conditions of the extract that exhibited the maximum activity have been determined and the biological activity of the extract that was obtained under these conditions was analyzed. TAS values were obtained from extracts obtained using extraction temperatures of 30-60 °C, extraction times of 4-10 h, and extract concentrations of 0.25-2 mg/mL. The best model selected from the established ANN models had a mean absolute percentage error (MAPE) value of 0.643%, a mean squared error (MSE) value of 0.004, and a correlation coefficient (R) value of 0.996, respectively. The genetic algorithm proposed optimal extraction conditions of an extraction temperature of 59.391 °C, an extraction time of 8.841 h, and an extraction concentration of 1.951 mg/mL. It was concluded that the integration of ANN-GA can successfully be used to optimize extraction parameters of Hypericum spectabile. The total antioxidant value of the extract obtained under optimum conditions was determined as 9.306 ± 0.080 mmol/L, total oxidant value as 13.065 ± 0.112 µmol/L, oxidative stress index as 0.140 ± 0.001. Total phenolic content (TPC) was 109.34 ± 1.29 mg/g, total flavonoid content (TFC) was measured as 148.34 ± 1.48 mg/g. Anti-AChE value was determined as 30.68 ± 0.77 µg/mL, anti-BChE value was determined as 41.30 ± 0.48 µg/mL. It was also observed that the extract exhibited strong antiproliferative activities depending on the increase in concentration. As a result of LC-MS/MS analysis of the extract produced under optimum conditions in terms of phenolic content. The presence of fumaric, gallic, protocatechuic, 4-hydroxybenzoic, caffeic, 2-hydoxycinamic acids, quercetin and kaempferol was detected. As a result, it was determined that the H. spectabile extract produced under optimum conditions had significant effects in terms of biological activity. Biology ; Artificial neural network ; Hypericum ; Algorithm ; Computational biology ; Artificial intelligence ; Computer science ; Botany Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Bioactive natural compounds
Authors: Hyun Jung Kim | Matthew Julian | Calum Williams | David Bombara | Juejun Hu | Tian Gu | Kiumars Aryana | Godfrey Sauti | William M. Humphreys
Source: npj Microgravity (article, 2024-02-20)
Field: Materials Science — Materials Chemistry

Versatile spaceborne photonics with chalcogenide phase-change materials Recent growth in space systems has seen increasing capabilities packed into smaller and lighter Earth observation and deep space mission spacecraft. Phase-change materials (PCMs) are nonvolatile, reconfigurable, fast-switching, and have recently shown a high degree of space radiation tolerance, thereby making them an attractive materials platform for spaceborne photonics applications. They promise robust, lightweight, and energy-efficient reconfigurable optical systems whose functions can be dynamically defined on-demand and on-orbit to deliver enhanced science or mission support in harsh environments on lean power budgets. This comment aims to discuss the recent advances in rapidly growing PCM research and its potential to transition from conventional terrestrial optoelectronics materials platforms to versatile spaceborne photonic materials platforms for current and next-generation space and science missions. Materials International Space Station Experiment-14 (MISSE-14) mission-flown PCMs outside of the International Space Station (ISS) and key results and NASA examples are highlighted to provide strong evidence of the applicability of spaceborne photonics. Photonics ; Spacecraft ; Chalcogenide ; Aerospace engineering ; Low earth orbit ; Earth observation ; Space exploration ; International Space Station ; Computer science ; NASA Deep Space Network ; Systems engineering ; Space Science ; Remote sensing ; Materials science ; Engineering ; Satellite ; Optoelectronics ; Geology Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Transition Metal Oxide Nanomaterials
Authors: Georgii Khusainov | J. Standfuss | Tobias Weinert
Source: Structural Dynamics (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

The time revolution in macromolecular crystallography Macromolecular crystallography has historically provided the atomic structures of proteins fundamental to cellular functions. However, the advent of cryo-electron microscopy for structure determination of large and increasingly smaller and flexible proteins signaled a paradigm shift in structural biology. The extensive structural and sequence data from crystallography and advanced sequencing techniques have been pivotal for training computational models for accurate structure prediction, unveiling the general fold of most proteins. Here, we present a perspective on the rise of time-resolved crystallography as the new frontier of macromolecular structure determination. We trace the evolution from the pioneering time-resolved crystallography methods to modern serial crystallography, highlighting the synergy between rapid detection technologies and state-of-the-art x-ray sources. These innovations are redefining our exploration of protein dynamics, with high-resolution crystallography uniquely positioned to elucidate rapid dynamic processes at ambient temperatures, thus deepening our understanding of protein functionality. We propose that the integration of dynamic structural data with machine learning advancements will unlock predictive capabilities for protein kinetics, revolutionizing dynamics like macromolecular crystallography revolutionized structural biology. Structural biology ; Electron crystallography ; Macromolecule ; Cryo-electron microscopy ; Protein structure ; Computer science ; Computational biology ; Chemistry ; Nanotechnology ; Crystallography ; Biophysics ; Biology ; Materials science ; Physics ; Diffraction ; Electron diffraction ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Leo Zella | Ji‐Won Moon | T. Egami
Source: Nature Communications (article, 2024-02-14)
Field: Materials Science — Materials Chemistry

Ripples in the bottom of the potential energy landscape of metallic glass In the absence of periodicity, the structure of glass is ill-defined, and a large number of structural states are found at similar energy levels. However, little is known about how these states are connected to each other in the potential energy landscape. We simulate mechanical relaxation by molecular dynamics for a prototypical [Formula: see text] metallic glass and follow the mechanical energy loss of each atom to track the change in the state. We find that the energy barriers separating these states are remarkably low, only of the order of 1 meV, implying that even quantum fluctuations can overcome these potential energy barriers. Our observation of numerous small ripples in the bottom of the potential energy landscape puts many assumptions regarding the thermodynamic states of metallic glasses into question and suggests that metallic glasses are not totally frozen at the local atomic level. Relaxation (psychology) ; Energy (signal processing) ; Atom (system on chip) ; Energy landscape ; Metal ; Materials science ; Physics ; Thermodynamics ; Computer science ; Quantum mechanics ; Metallurgy Material Dynamics and Properties ; Metallic Glasses and Amorphous Alloys ; Theoretical and Computational Physics