jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Li Fu | Cheng‐Te Lin | Hassan Karimi‐Maleh | Fei Chen | Shichao Zhao
Source: Biosensors (review, 2023-11-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonic Nanoparticle-Enhanced Optical Techniques for Cancer Biomarker Sensing This review summarizes recent advances in leveraging localized surface plasmon resonance (LSPR) nanotechnology for sensitive cancer biomarker detection. LSPR arising from noble metal nanoparticles under light excitation enables the enhancement of various optical techniques, including surface-enhanced Raman spectroscopy (SERS), dark-field microscopy (DFM), photothermal imaging, and photoacoustic imaging. Nanoparticle engineering strategies are discussed to optimize LSPR for maximum signal amplification. SERS utilizes electromagnetic enhancement from plasmonic nanostructures to boost inherently weak Raman signals, enabling single-molecule sensitivity for detecting proteins, nucleic acids, and exosomes. DFM visualizes LSPR nanoparticles based on scattered light color, allowing for the ultrasensitive detection of cancer cells, microRNAs, and proteins. Photothermal imaging employs LSPR nanoparticles as contrast agents that convert light to heat, producing thermal images that highlight cancerous tissues. Photoacoustic imaging detects ultrasonic waves generated by LSPR nanoparticle photothermal expansion for deep-tissue imaging. The multiplexing capabilities of LSPR techniques and integration with microfluidics and point-of-care devices are reviewed. Remaining challenges, such as toxicity, standardization, and clinical sample analysis, are examined. Overall, LSPR nanotechnology shows tremendous potential for advancing cancer screening, diagnosis, and treatment monitoring through the integration of nanoparticle engineering, optical techniques, and microscale device platforms. Photothermal therapy ; Nanotechnology ; Materials science ; Surface plasmon resonance ; Plasmonic nanoparticles ; Nanoparticle ; Plasmon ; Nanomedicine ; Surface-enhanced Raman spectroscopy ; Cancer biomarkers ; Microfluidics ; Colloidal gold ; Raman spectroscopy ; Optoelectronics ; Raman scattering ; Cancer ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Photoacoustic and Ultrasonic Imaging ; Nanoplatforms for cancer theranostics
Authors: Cheng‐Wei Ju | Xi–Chao Wang | Bo Li | Qiu-Shi Ma | Yuhao Shi | Jinyu Zhang | Yuzhi Xu | Qian Peng | Dongbing Zhao
Source: Proceedings of the National Academy of Sciences (article, 2023-11-07)
Field: Materials Science — Materials Chemistry

Evolution of organic phosphor through precision regulation of nonradiative decay Development of single-component organic phosphor attracts increasing interest due to its wide applications in optoelectronic technologies. Theoretically, activating efficient intersystem crossing (ISC) via <sup>1</sup>(π, π*) to <sup>3</sup>(π, π*) transitions, rather than <sup>1</sup>(n, π*) → <sup>3</sup>(π, π*) transitions, is an alternative access to purely organic phosphors but remains challenging. Herein, we designed and successfully synthesized the sila-8-membered ring fused biaryl benzoskeleton by transition metal catalysis, which served as a new organic phosphor with efficient <sup>1</sup>(π, π*) to <sup>3</sup>(π, π*) ISC. We first found that such a compound exhibits a record-long phosphorescence lifetime of 6.5 s at low temperature for single-component organic systems. Then, we developed two strategies to tune their decay channels to evolve such nonemissive molecules into bright phosphors with elongated lifetimes at room temperature: 1) Physic-based design, where quantitative analyses of electron-phonon coupling led us to reveal and hinder the major nonradiative channels, thus lighted up room temperature phosphorescence (RTP) with a lifetime of 480 ms at 298 K; 2) chemical geometry-driven molecular engineering, where a geometry-based descriptor ΔΘ<sub>T1-S0</sub>/Θ<sub>S0</sub> was developed for rational screening RTP candidates and further improved the RTP lifetime to 794 ms. This study clearly shows the power of interdiscipline among synthetic methodology, physics-based rational design, and computational modeling, which represents a paradigm for the development of an organic emitter. Phosphorescence ; Phosphor ; Intersystem crossing ; Rational design ; Common emitter ; Luminescence ; Component (thermodynamics) ; Optoelectronics ; Materials science ; Chemistry ; Photochemistry ; Nanotechnology ; Chemical physics ; Fluorescence ; Physics ; Atomic physics ; Thermodynamics ; Excited state ; Optics Luminescence and Fluorescent Materials ; Organoboron and organosilicon chemistry ; Perovskite Materials and Applications
Authors: Tathagata Biswas | Arunima K. Singh
Source: npj Computational Materials (article, 2023-02-13)
Field: Materials Science — Materials Chemistry

pyGWBSE: a high throughput workflow package for GW-BSE calculations Abstract We develop an open-source python workflow package, py GWBSE to perform automated first-principles calculations within the GW-BSE (Bethe-Salpeter) framework. GW-BSE is a many body perturbation theory based approach to explore the quasiparticle (QP) and excitonic properties of materials. GW approximation accurately predicts bandgaps of materials by overcoming the bandgap underestimation issue of the more widely used density functional theory (DFT). BSE formalism produces absorption spectra directly comparable with experimental observations. py GWBSE package achieves complete automation of the entire multi-step GW-BSE computation, including the convergence tests of several parameters that are crucial for the accuracy of these calculations. py GWBSE is integrated with Wannier90 , to generate QP bandstructures, interpolated using the maximally-localized wannier functions. py GWBSE also enables the automated creation of databases of metadata and data, including QP and excitonic properties, which can be extremely useful for future material discovery studies in the field of ultra-wide bandgap semiconductors, electronics, photovoltaics, and photocatalysis. Workflow ; Computation ; Python (programming language) ; Band gap ; Computer science ; Semiconductor ; Quasiparticle ; Electronics ; Scripting language ; Interoperability ; Density functional theory ; Computational science ; Physics ; Optoelectronics ; Algorithm ; Engineering ; Quantum mechanics ; Electrical engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Physics of Superconductivity and Magnetism
Authors: Yuchen Shang | Mingguang Yao | Zhaodong Liu | Rong Fu | Longbiao Yan | Long Yang | Zhongyin Zhang | Jiajun Dong | Chunguang Zhai | Xuyuan Hou | Liting Fei | Guanjie Zhang | Jianfeng Ji | Jie Zhu | He Lin | Bertil Sundqvist | Bingbing Liu
Source: Nature Communications (article, 2023-11-29)
Field: Materials Science — Materials Chemistry

Enhancement of short/medium-range order and thermal conductivity in ultrahard sp3 amorphous carbon by C70 precursor As an advanced amorphous material, sp<sup>3</sup> amorphous carbon exhibits exceptional mechanical, thermal and optical properties, but it cannot be synthesized by using traditional processes such as fast cooling liquid carbon and an efficient strategy to tune its structure and properties is thus lacking. Here we show that the structures and physical properties of sp<sup>3</sup> amorphous carbon can be modified by changing the concentration of carbon pentagons and hexagons in the fullerene precursor from the topological transition point of view. A highly transparent, nearly pure sp<sup>3</sup>-hybridized bulk amorphous carbon, which inherits more hexagonal-diamond structural feature, was synthesized from C<sub>70</sub> at high pressure and high temperature. This amorphous carbon shows more hexagonal-diamond-like clusters, stronger short/medium-range structural order, and significantly enhanced thermal conductivity (36.3 ± 2.2 W m<sup>-1</sup> K<sup>-1</sup>) and higher hardness (109.8 ± 5.6 GPa) compared to that synthesized from C<sub>60</sub>. Our work thus provides a valid strategy to modify the microstructure of amorphous solids for desirable properties. Amorphous carbon ; Materials science ; Amorphous solid ; Carbon fibers ; Diamond ; Microstructure ; Chemical engineering ; Thermal conductivity ; Conductivity ; Fullerene ; Chemical physics ; Nanotechnology ; Topology (electrical circuits) ; Composite material ; Crystallography ; Organic chemistry ; Chemistry ; Physical chemistry ; Composite number Diamond and Carbon-based Materials Research ; Fullerene Chemistry and Applications ; Boron and Carbon Nanomaterials Research
Authors: Ryoichi Furushima | Yuki Nakashima | Yutaka Maruyama | Kiyoshi Hirao | Tatsuki Ohji | Manabu Fukushima
Source: Journal of the American Ceramic Society (article, 2023-04-14)
Field: Materials Science — Ceramics and Composites

Artificial Intelligence‐based determination of fracture toughness and bending strength of silicon nitride ceramics Abstract Two mechanical properties, fracture toughness ( K IC ) and bending strength ( σ ), of silicon nitride (Si 3 N 4 ) ceramics were determined from their microstructural images via convolutional neural network (CNN) models. The Si 3 N 4 samples used for database were fabricated using various kinds of sintering additives under different process conditions. In total, 330 data sets were prepared and used for building the CNN models for artificial intelligence‐bassed determination of the two mechanical properties and testing the determination accuracy of the trained models. The determination coefficients ( R 2 ), which were used as accuracy indices, were approximately 0.85 for K IC and 0.92 for σ . Although both the R 2 values were relatively high, the lower value for K IC suggests that it is influenced more by what is little obtained from the microstructural information, such as grain‐boundary characteristics. Furthermore, gradient‐weighted class activation mapping, which can visualize which parts of the image the CNN models focus on, showed that the trained models determined the two mechanical properties based on correct recognition of the microstructural difference among the images. Silicon nitride ; Fracture toughness ; Materials science ; Ceramic ; Flexural strength ; Convolutional neural network ; Composite material ; Bending ; Sintering ; Artificial neural network ; Silicon ; Artificial intelligence ; Computer science ; Metallurgy Advanced ceramic materials synthesis ; Advanced materials and composites ; Rock Mechanics and Modeling
Authors: Emma Danelius | Nicholas J. Porter | Johan Unge | Frances H. Arnold | Tamir Gonen
Source: Journal of the American Chemical Society (article, 2023-03-22)
Field: Chemistry — Spectroscopy

MicroED Structure of a Protoglobin Reactive Carbene Intermediate Microcrystal electron diffraction (MicroED) is an emerging technique that has shown great potential for describing new chemical and biological molecular structures. Several important structures of small molecules, natural products, and peptides have been determined using <i>ab initio</i> methods. However, only a couple of novel protein structures have thus far been derived by MicroED. Taking advantage of recent technological advances, including higher acceleration voltage and using a low-noise detector in counting mode, we have determined the first structure of an <i>Aeropyrum pernix</i> protoglobin (<i>Ape</i>Pgb) variant by MicroED using an AlphaFold2 model for phasing. The structure revealed that mutations introduced during directed evolution enhance carbene transfer activity by reorienting an α helix of <i>Ape</i>Pgb into a dynamic loop, making the catalytic active site more readily accessible. After exposing the tiny crystals to the substrate, we also trapped the reactive iron-carbenoid intermediate involved in this engineered <i>Ape</i>Pgb's new-to-nature activity, a challenging carbene transfer from a diazirine via a putative metallo-carbene. The bound structure discloses how an enlarged active site pocket stabilizes the carbene bound to the heme iron and, presumably, the transition state for the formation of this key intermediate. This work demonstrates that improved MicroED technology and the advancement in protein structure prediction now enable investigation of structures that was previously beyond reach. Chemistry ; Carbene ; Helix (gastropod) ; Chemical physics ; Nanotechnology ; Crystallography ; Catalysis Mass Spectrometry Techniques and Applications ; Enzyme Structure and Function ; Hemoglobin structure and function
Authors: Mostafa Yousefzadeh Borzehandani | Majid Namayandeh Jorabchi | Emilia Abdulmalek | Mohd Basyaruddin Abdul Rahman | Muhammad Alif Mohammad Latif
Source: Polymers (article, 2023-02-02)
Field: Chemistry — Inorganic Chemistry

Exploring the Potential of a Highly Scalable Metal-Organic Framework CALF-20 for Selective Gas Adsorption at Low Pressure In this study, the ability of the highly scalable metal-organic framework (MOF) CALF-20 to adsorb polar and non-polar gases at low pressure was investigated using grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations. The results from the simulated adsorption isotherms revealed that the highest loading was achieved for SO<sub>2</sub> and Cl<sub>2</sub>, while the lowest loading was found for F<sub>2</sub> molecules. The analysis of interaction energies indicated that SO<sub>2</sub> molecules were able to form the strongest adsorbent-adsorbate interactions and had a tight molecular packing due to their polarity and angular structure. Additionally, Cl<sub>2</sub> gas was found to be highly adsorbed due to its large van der Waals surface and strong chemical affinity in CALF-20 pores. MD simulations showed that SO<sub>2</sub> and Cl<sub>2</sub> had the lowest mobility inside CALF-20 pores. The values of the Henry coefficient and isosteric heat of adsorption confirmed that CALF-20 could selectively adsorb SO<sub>2</sub> and Cl<sub>2</sub>. Based on the results, it was concluded that CALF-20 is a suitable adsorbent for SO<sub>2</sub> and Cl<sub>2</sub> but not for F<sub>2</sub>. This research emphasizes the importance of molecular size, geometry, and polarity in determining the suitability of a porous material as an adsorbent for specific adsorbates. Adsorption ; van der Waals force ; Chemical polarity ; Molecule ; Molecular dynamics ; Metal-organic framework ; Polarity (international relations) ; Chemical physics ; Materials science ; Polar ; Chemistry ; Porosity ; Chemical engineering ; Thermodynamics ; Physical chemistry ; Computational chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Nanoplatforms for cancer theranostics
Authors: Elías Cueto | Francisco Chinesta
Source: Archives of Computational Methods in Engineering (article, 2023-06-14)
Field: Materials Science — Materials Chemistry

Thermodynamics of Learning Physical Phenomena Abstract Thermodynamics could be seen as an expression of physics at a high epistemic level. As such, its potential as an inductive bias to help machine learning procedures attain accurate and credible predictions has been recently realized in many fields. We review how thermodynamics provides helpful insights in the learning process. At the same time, we study the influence of aspects such as the scale at which a given phenomenon is to be described, the choice of relevant variables for this description or the different techniques available for the learning process. Process (computing) ; Scale (ratio) ; Phenomenon ; Computer science ; Statistical physics ; Machine learning ; Artificial intelligence ; Physics ; Epistemology ; Philosophy ; Quantum mechanics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Protein Structure and Dynamics
Authors: Anna Scarsi | Deborah Pedone | Pier Paolo Pompa
Source: Nanoscale Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

A multi-line platinum nanozyme-based lateral flow device for the colorimetric evaluation of total antioxidant capacity in different matrices The evaluation of Total Antioxidant Capacity (TAC), namely the complete pattern of antioxidant species in a complex medium, is of major interest in many fields ranging from health monitoring to quality control in the food industry. In this framework, point-of-care (POC) testing technologies are a promising diagnostic solution for rapid on-site analyses, unlike laboratory based-assays, which are often limited by centralized analyses, time-consuming and costly procedures, and invasiveness in the case of health diagnostics. In this work, we developed a POC methodology that evaluates TAC in different matrices, exploiting the peroxidase-like properties of 5 nm platinum nanoparticles (PtNPs), combined with a colorimetric paper-based device. Notably, we designed and optimized a multi-line PtNPs-based Lateral Flow Assay (LFA), which relies on three sequential test lines with increasing concentrations of platinum nanozymes, to get a non-invasive, accurate, and fast (10 minutes) colorimetric evaluation of the body TAC in saliva samples. Furthermore, we employed the device as a prototype of a quality control tool in the food industry, for the determination of the TAC in fruit juices. Antioxidant capacity ; Platinum nanoparticles ; Food industry ; Computer science ; Platinum ; Antioxidant ; Chemistry ; Process engineering ; Nanotechnology ; Environmental science ; Materials science ; Food science ; Biochemistry ; Engineering Advanced Nanomaterials in Catalysis ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Takehito Seki | Toshihiro Futazuka | Nobusato Morishige | Ryo Matsubara | Yuichi Ikuhara | Naoya Shibata
Source: Nature Communications (article, 2023-12-05)
Field: Materials Science — Materials Chemistry

Incommensurate grain-boundary atomic structure Grain-boundary atomic structures of crystalline materials have long been believed to be commensurate with the crystal periodicity of the adjacent crystals. In the present study, we experimentally observed a Σ9 grain-boundary atomic structure of a bcc crystal (Fe-3%Si). It is found that the Σ9 grain-boundary structure is largely reconstructed and forms a dense packing of icosahedral clusters in its core. Combining with the detailed theoretical calculations, the Σ9 grain-boundary atomic structure is discovered to be incommensurate with the adjacent crystal structures. The present findings shed new light on the study of stable grain-boundary atomic structures in crystalline materials. Grain boundary ; Icosahedral symmetry ; Crystal structure ; Materials science ; Crystallography ; Core (optical fiber) ; Boundary (topology) ; Crystal (programming language) ; Condensed matter physics ; Chemical physics ; Chemistry ; Physics ; Microstructure ; Composite material ; Mathematics Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels ; Magnetic Properties and Applications
Authors: Kevin Eloff | Konstantinos Kalogeropoulos | Oliver Morell | Amandla Mabona | Jakob Berg Jespersen | Wesley Williams | Sam P. B. van Beljouw | Marcin J. Skwark | Andreas H. Laustsen | Stan J. J. Brouns | Anne Ljungers | Erwin M. Schoof | Jeroen Van Goey | Ulrich auf dem Keller | Karim Beguir | Nicolás López Carranza | Timothy P. Jenkins
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-08-31)
Field: Chemistry — Spectroscopy

De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments Abstract Bottom-up mass spectrometry-based proteomics is challenged by the task of identifying the peptide that generates a tandem mass spectrum. Traditional methods that rely on known peptide sequence databases are limited and may not be applicable in certain contexts. De novo peptide sequencing, which assigns peptide sequences to the spectra without prior information, is valuable for various biological applications; yet, due to a lack of accuracy, it remains challenging to apply this approach in many situations. Here, we introduce InstaNovo, a transformer neural network with the ability to translate fragment ion peaks into the sequence of amino acids that make up the studied peptide(s). The model was trained on 28 million labelled spectra matched to 742k human peptides from the ProteomeTools project. We demonstrate that InstaNovo outperforms current state-of-the-art methods on benchmark datasets and showcase its utility in several applications. Building upon human intuition, we also introduce InstaNovo+, a multinomial diffusion model that further improves performance by iterative refinement of predicted sequences. Using these models, we could de novo sequence antibody-based therapeutics with unprecedented coverage, discover novel peptides, and detect unreported organisms in different datasets, thereby expanding the scope and detection rate of proteomics searches. Finally, we could experimentally validate tryptic and non-tryptic peptides with targeted proteomics, demonstrating the fidelity of our predictions. Our models unlock a plethora of opportunities across different scientific domains, such as direct protein sequencing, immunopeptidomics, and exploration of the dark proteome. Proteomics ; Computational biology ; Proteome ; Computer science ; Peptide ; Tandem mass spectrometry ; Shotgun proteomics ; Human proteome project ; Proteogenomics ; Peptide sequence ; Intuition ; Bottom-up proteomics ; Biology ; Mass spectrometry ; Bioinformatics ; Genome ; Chemistry ; Genomics ; Biochemistry ; Gene ; Protein mass spectrometry ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; RNA and protein synthesis mechanisms
Authors: Xunlan Li | Zhaoxin Wei | Fangfang Peng | Jianfei Liu | Guohui Han
Source: Frontiers in Plant Science (article, 2023-03-27)
Field: Chemistry — Analytical Chemistry

Non-destructive prediction and visualization of anthocyanin content in mulberry fruits using hyperspectral imaging Being rich in anthocyanin is one of the most important physiological traits of mulberry fruits. Efficient and non-destructive detection of anthocyanin content and distribution in fruits is important for the breeding, cultivation, harvesting and selling of them. This study aims at building a fast, non-destructive, and high-precision method for detecting and visualizing anthocyanin content of mulberry fruit by using hyperspectral imaging. Visible near-infrared hyperspectral images of the fruits of two varieties at three maturity stages are collected. Successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and stacked auto-encoder (SAE) are used to reduce the dimension of high-dimensional hyperspectral data. The least squares-support vector machine and extreme learning machine (ELM) are used to build models for predicting the anthocyanin content of mulberry fruit. And genetic algorithm (GA) is used to optimize the major parameters of models. The results show that the higher the anthocyanin content is, the lower the spectral reflectance is. 15, 7 and 13 characteristic variables are extracted by applying CARS, SPA and SAE respectively. The model based on SAE-GA-ELM achieved the best performance with R<sup>2</sup> of 0.97 and the RMSE of 0.22 mg/g in both the training set and testing set, and it is applied to retrieve the distribution of anthocyanin content in mulberry fruits. By applying SAE-GA-ELM model to each pixel of the mulberry fruit images, distribution maps are created to visualize the changes in anthocyanin content of mulberry fruits at three maturity stages. The overall results indicate that hyperspectral imaging, in combination with SAE-GA-ELM, can help achieve rapid, non-destructive and high-precision detection and visualization of anthocyanin content in mulberry fruits. Hyperspectral imaging ; Anthocyanin ; Mathematics ; Pixel ; Artificial intelligence ; Computer science ; Biological system ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Phytochemicals and Antioxidant Activities