jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yanhui Wang | Rongxin Li | Weikang Shu | Xiaonan Chen | Yingying Lin | Jingjing Wan
Source: Small Methods (review, 2023-11-03)
Field: Chemistry — Spectroscopy

Designed Nanomaterials‐Assisted Proteomics and Metabolomics Analysis for In Vitro Diagnosis In vitro diagnosis (IVD) is pivotal in modern medicine, enabling early disease detection and treatment optimization. Omics technologies, particularly proteomics and metabolomics, offer profound insights into IVD. Despite its significance, omics analyses for IVD face challenges, including low analyte concentrations and the complexity of biological environments. In addition, the direct omics analysis by mass spectrometry (MS) is often hampered by issues like large sample volume requirements and poor ionization efficiency. Through manipulating their size, surface charge, and functionalization, as well as the nanoparticle-fluid incubation conditions, nanomaterials have emerged as a promising solution to extract biomolecules and enhance the desorption/ionization efficiency in MS detection. This review delves into the last five years of nanomaterial applications in omics, focusing on their role in the enrichment, separation, and ionization analysis of proteins and metabolites for IVD. It aims to provide a comprehensive update on nanomaterial design and application in omics, highlighting their potential to revolutionize IVD. Metabolomics ; Proteomics ; Omics ; Nanotechnology ; Computational biology ; Nanomaterials ; Chemistry ; Biochemical engineering ; Biology ; Bioinformatics ; Materials science ; Engineering ; Biochemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Paula Alarcon‐Espejo | Ruben Sarabia‐Riquelme | Giovanni Maria Matrone | Maryam Shahi | Siamak Mahmoudi | Gehan S. Rupasinghe | Vianna N. Le | Antonio M. Mántica | Dali Qian | T. John Balk | Jonathan Rivnay | Matthew C. Weisenberger | Alexandra F. Paterson
Source: Advanced Materials (article, 2023-10-12)
Field: Materials Science — Polymers and Plastics

High‐Hole‐Mobility Fiber Organic Electrochemical Transistors for Next‐Generation Adaptive Neuromorphic Bio‐Hybrid Technologies The latest developments in fiber design and materials science are paving the way for fibers to evolve from parts in passive components to functional parts in active fabrics. Designing conformable, organic electrochemical transistor (OECT) structures using poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) fibers has excellent potential for low-cost wearable bioelectronics, bio-hybrid devices, and adaptive neuromorphic technologies. However, to achieve high-performance, stable devices from PEDOT:PSS fibers, approaches are required to form electrodes on fibers with small diameters and poor wettability, that leads to irregular coatings. Additionally, PEDOT:PSS-fiber fabrication needs to move away from small batch processing to roll-to-roll or continuous processing. Here, it is shown that synergistic effects from a superior electrode/organic interface, and exceptional fiber alignment from continuous processing, enable PEDOT:PSS fiber-OECTs with stable contacts, high µC* product (1570.5 F cm<sup>-1</sup> V<sup>-1</sup> s<sup>-1</sup> ), and high hole mobility over 45 cm<sup>2</sup> V<sup>-1</sup> s<sup>-1</sup> . Fiber-electrochemical neuromorphic organic devices (fiber-ENODes) are developed to demonstrate that the high mobility fibers are promising building blocks for future bio-hybrid technologies. The fiber-ENODes demonstrate synaptic weight update in response to dopamine, as well as a form factor closely matching the neuronal axon terminal. PEDOT:PSS ; Materials science ; Bioelectronics ; Neuromorphic engineering ; Conformable matrix ; Fiber ; Nanotechnology ; Electrode ; Conductive polymer ; Transistor ; Optoelectronics ; Polymer ; Biosensor ; Computer science ; Voltage ; Composite material ; Electrical engineering ; Layer (electronics) Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Supercapacitor Materials and Fabrication
Authors: Jing Chen | Rong Fei | Yibing Xie
Source: Materials (review, 2023-04-23)
Field: Materials Science — Biomaterials

Fabrication, Microstructures and Sensor Applications of Highly Ordered Electrospun Nanofibers: A Review The review summarizes the fabrication, microstructures, and sensor applications of highly ordered electrospun nanofibers. In the traditional electrospinning process, electrospun nanofibers usually have disordered or random microstructures due to the chaotic oscillation of the electrospinning jet. Different electrospinning methods can be formed by introducing external forces, such as magnetic, electric, or mechanical forces, and ordered nanofibers can be collected. The microstructures of highly ordered nanofibers can be divided into three categories: uniaxially ordered nanofibers, biaxially ordered nanofibers and ordered scaffolds. The three microstructures are each characterized by being ordered in different dimensions. The regulation and control of the ordered microstructures can promote electrospun nanofibers' mechanical and dielectric strength, surface area and chemical properties. Highly ordered electrospun nanofibers have more comprehensive applications than disordered nanofibers do in effect transistors, gas sensors, reinforced composite materials and tissue engineering. This review also intensively summarizes the applications of highly ordered nanofibers in the sensor field, such as pressure sensors, humidity sensors, strain sensors, gas sensors, and biosensors. Fabrication ; Materials science ; Nanofiber ; Microstructure ; Electrospinning ; Nanotechnology ; Composite material ; Polymer Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Matthew G. Boebinger | Courtney Brea | Liping Ding | Sudhajit Misra | Olugbenga Olunloyo | Yiling Yu | Kai Xiao | Andrew R. Lupini | Feng Ding | Guoxiang Hu | Panchapakesan Ganesh | Stephen Jesse | Raymond R. Unocic
Source: Advanced Materials (article, 2023-01-12)
Field: Materials Science — Materials Chemistry

The Atomic Drill Bit: Precision Controlled Atomic Fabrication of 2D Materials The ability to deterministically fabricate nanoscale architectures with atomic precision is the central goal of nanotechnology, whereby highly localized changes in the atomic structure can be exploited to control device properties at their fundamental physical limit. Here, an automated, feedback-controlled atomic fabrication method is reported and the formation of 1D-2D heterostructures in MoS<sub>2</sub> is demonstrated through selective transformations along specific crystallographic orientations. The atomic-scale probe of an aberration-corrected scanning transmission electron microscope (STEM) is used, and the shape and symmetry of the scan pathway relative to the sample orientation are controlled. The focused and shaped electron beam is used to reliably create Mo<sub>6</sub> S<sub>6</sub> nanowire (MoS-NW) terminated metallic-semiconductor 1D-2D edge structures within a pristine MoS<sub>2</sub> monolayer with atomic precision. From these results, it is found that a triangular beam path aligned along the zig-zag sulfur terminated (ZZS) direction forms stable MoS-NW edge structures with the highest degree of fidelity without resulting in disordering of the surrounding MoS<sub>2</sub> monolayer. Density functional theory (DFT) calculations and ab initio molecular dynamic simulations (AIMD) are used to calculate the energetic barriers for the most stable atomic edge structures and atomic transformation pathways. These discoveries provide an automated method to improve understanding of atomic-scale transformations while opening a pathway toward more precise atomic-scale engineering of materials. Atomic units ; Materials science ; Scanning transmission electron microscopy ; Nanowire ; Fabrication ; Monolayer ; Nanotechnology ; Nanoscopic scale ; Enhanced Data Rates for GSM Evolution ; Nanolithography ; Optoelectronics ; Chemical physics ; Transmission electron microscopy ; Chemistry ; Physics 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Nanowire Synthesis and Applications
Authors: Haiteng Zhao | Shengchao Liu | Chang Ma | Hannan Xu | Jie Fu | Zhi‐Hong Deng | Lingpeng Kong | Qi Liu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-06-01)
Field: Materials Science — Materials Chemistry

GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning Abstract Molecule property prediction has gained significant attention in recent years. The main bottleneck is the label insufficiency caused by expensive lab experiments. In order to alleviate this issue and to better leverage textual knowledge for tasks, this study investigates the feasibility of employing natural language instructions to accomplish molecule-related tasks in a zero-shot setting. We discover that existing molecule-text models perform poorly in this setting due to inadequate treatment of instructions and limited capacity for graphs. To overcome these issues, we propose GIMLET, which unifies language models for both graph and text data. By adopting generalized position embedding, our model is extended to encode both graph structures and instruction text without additional graph encoding modules. GIMLET also decouples encoding of the graph from tasks instructions in the attention mechanism, enhancing the generalization of graph features across novel tasks. We construct a dataset consisting of more than two thousand molecule tasks with corresponding instructions derived from task descriptions. We pretrain GIMLET on the molecule tasks along with instructions, enabling the model to transfer effectively to a broad range of tasks. Experimental results demonstrate that GIMLET significantly outperforms molecule-text baselines in instruction-based zero-shot learning, even achieving closed results to supervised GNN models on tasks such as toxcast and muv. 1 Computer science ; Leverage (statistics) ; Graph ; Bottleneck ; Embedding ; Zero (linguistics) ; Theoretical computer science ; Generalization ; ENCODE ; Artificial intelligence ; Natural language processing ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jiaming Zhong | Wei Huang | Huamin Zhou
Source: Biomimetics (review, 2023-07-02)
Field: Materials Science — Biomaterials

Multifunctionality in Nature: Structure–Function Relationships in Biological Materials Modern material design aims to achieve multifunctionality through integrating structures in a diverse range, resulting in simple materials with embedded functions. Biological materials and organisms are typical examples of this concept, where complex functionalities are achieved through a limited material base. This review highlights the multiscale structural and functional integration of representative natural organisms and materials, as well as biomimetic examples. The impact, wear, and crush resistance properties exhibited by mantis shrimp and ironclad beetle during predation or resistance offer valuable inspiration for the development of structural materials in the aerospace field. Investigating cyanobacteria that thrive in extreme environments can contribute to developing living materials that can serve in places like Mars. The exploration of shape memory and the self-repairing properties of spider silk and mussels, as well as the investigation of sensing-actuating and sensing-camouflage mechanisms in Banksias, chameleons, and moths, holds significant potential for the optimization of soft robot designs. Furthermore, a deeper understanding of mussel and gecko adhesion mechanisms can have a profound impact on medical fields, including tissue engineering and drug delivery. In conclusion, the integration of structure and function is crucial for driving innovations and breakthroughs in modern engineering materials and their applications. The gaps between current biomimetic designs and natural organisms are also discussed. Function (biology) ; Structure function ; Materials science ; Biological system ; Physics ; Biology ; Evolutionary biology Calcium Carbonate Crystallization and Inhibition ; Silk-based biomaterials and applications ; Cephalopods and Marine Biology
Authors: Wei Liu | James Mulhearn | Bo Hao | Santiago Cañellas | Stefaan Last | José Enrique Gómez | Alexander X. Jones | Alexander De Vera | Kiran Kumar | Raquel Rodríguez | Lars Van Eynde | Iulia I. Strambeanu | S. E. Wolkenberg
Source: ACS Medicinal Chemistry Letters (article, 2023-05-22)
Field: Chemistry — Organic Chemistry

Enabling Deoxygenative C(sp<sup>2</sup>)-C(sp<sup>3</sup>) Cross-Coupling for Parallel Medicinal Chemistry Herein we report the development of an automated deoxygenative C(sp<sup>2</sup>)-C(sp<sup>3</sup>) coupling of aryl bromide with alcohols to enable parallel medicinal chemistry. Alcohols are among the most diverse and abundant building blocks, but their usage as alkyl precursors has been limited. Although metallaphotoredox deoxygenative coupling is becoming a promising strategy to form C(sp<sup>2</sup>)-C(sp<sup>3</sup>) bond, the reaction setup limits its widespread application in library synthesis. To achieve high throughput and consistency, an automated workflow involving solid-dosing and liquid-handling robots has been developed. We have successfully demonstrated this high-throughput protocol is robust and consistent across three automation platforms. Furthermore, guided by cheminformatic analysis, we examined alcohols with comprehensive chemical space coverage and established a meaningful scope for medicinal chemistry applications. By accessing the rich diversity of alcohols, this automated protocol has the potential to substantially increase the impact of C(sp<sup>2</sup>)-C(sp<sup>3</sup>) cross-coupling in drug discovery. Combinatorial chemistry ; Workflow ; Chemistry ; Drug discovery ; Computer science ; Chemical space ; Throughput ; Automation ; Database ; Engineering ; Biochemistry Catalytic Cross-Coupling Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation ; Radical Photochemical Reactions
Authors: Alexandar L. Hansen | Xinyao Xiang | Chunhua Yuan | Lei Bruschweiler‐Li | Rafael Brüschweiler
Source: Nature Structural & Molecular Biology (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

Excited-state observation of active K-Ras reveals differential structural dynamics of wild-type versus oncogenic G12D and G12C mutants Despite the prominent role of the K-Ras protein in many different types of human cancer, major gaps in atomic-level information severely limit our understanding of its functions in health and disease. Here, we report the quantitative backbone structural dynamics of K-Ras by solution nuclear magnetic resonance spectroscopy of the active state of wild-type K-Ras bound to guanosine triphosphate (GTP) nucleotide and two of its oncogenic P-loop mutants, G12D and G12C, using a new nanoparticle-assisted spin relaxation method, relaxation dispersion and chemical exchange saturation transfer experiments covering the entire range of timescales from picoseconds to milliseconds. Our combined experiments allow detection and analysis of the functionally critical Switch I and Switch II regions, which have previously remained largely unobservable by X-ray crystallography and nuclear magnetic resonance spectroscopy. Our data reveal cooperative transitions of K-Ras·GTP to a highly dynamic excited state that closely resembles the partially disordered K-Ras·GDP state. These results advance our understanding of differential GTPase activities and signaling properties of the wild type versus mutants and may thus guide new strategies for the development of therapeutics. GTP' ; Protein dynamics ; Guanosine triphosphate ; Chemistry ; Wild type ; Mutant ; Excited state ; Biophysics ; Crystallography ; Protein structure ; Biology ; Biochemistry ; Physics ; Atomic physics ; Gene Enzyme Structure and Function ; Protein Kinase Regulation and GTPase Signaling ; Protein Structure and Dynamics
Authors: David H. Kenney | Randy Paffenroth | Michaël T. Timko | Andrew R. Teixeira
Source: Journal of Cheminformatics (article, 2023-01-19)
Field: Chemistry — Analytical Chemistry

Dimensionally reduced machine learning model for predicting single component octanol–water partition coefficients MF-LOGP, a new method for determining a single component octanol-water partition coefficients ([Formula: see text]) is presented which uses molecular formula as the only input. Octanol-water partition coefficients are useful in many applications, ranging from environmental fate and drug delivery. Currently, partition coefficients are either experimentally measured or predicted as a function of structural fragments, topological descriptors, or thermodynamic properties known or calculated from precise molecular structures. The MF-LOGP method presented here differs from classical methods as it does not require any structural information and uses molecular formula as the sole model input. MF-LOGP is therefore useful for situations in which the structure is unknown or where the use of a low dimensional, easily automatable, and computationally inexpensive calculations is required. MF-LOGP is a random forest algorithm that is trained and tested on 15,377 data points, using 10 features derived from the molecular formula to make [Formula: see text] predictions. Using an independent validation set of 2713 data points, MF-LOGP was found to have an average [Formula: see text] = 0.77 ± 0.007, [Formula: see text] = 0.52 ± 0.003, and [Formula: see text] = 0.83 ± 0.003. This performance fell within the spectrum of performances reported in the published literature for conventional higher dimensional models ([Formula: see text] = 0.42-1.54, [Formula: see text] = 0.09-1.07, and [Formula: see text] = 0.32-0.95). Compared with existing models, MF-LOGP requires a maximum of ten features and no structural information, thereby providing a practical and yet predictive tool. The development of MF-LOGP provides the groundwork for development of more physical prediction models leveraging big data analytical methods or complex multicomponent mixtures. Component (thermodynamics) ; Computer science ; Partition coefficient ; Octanol ; Partition (number theory) ; Machine learning ; Artificial intelligence ; Data mining ; Chromatography ; Chemistry ; Mathematics ; Thermodynamics Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Idris Nur Permadi | Dini Basiratun Nisa
Source: Jurnal Riset Ilmu Teknik (article, 2023-05-31)
Field: Materials Science — Materials Chemistry

A Model Experiment Design Using the Taguchi Method: A Case Study Of Making Concrete Roof This research was conducted at a company manufacturing paving blocks, ceramics, ventilation, concrete, and roof tiles. This study aims to improve the quality of concrete tile products, which experience the most cracks and breaks. Research to determine the composition of concrete roof tiles can be carried out by design experiments using the Taguchi method. This experiment used raw materials such as cement, sand, milled flour, and water. The concluding results show that the quality of concrete roof tiles is based on the results of the Taguchi method and confirmation experiments which are characterized by an increase in the compressive strength of the concrete tile based on the average value (μ) and its variability (SNR). The average compressive strength (µ) of concrete roof tiles increased from 19,814 ≤ 21,208 ≤ 22,602 in the Taguchi experiment to 26,406 ≤ µconfirmation ≤ 29,894 in the confirmation experiment. While the variability (SNR) increased from 25.532 ≤ 27.521 ≤ 29.510 in the Taguchi experiment to 26.473 ≤ S/N confirmed ≤ 31.469 in the confirmation experiment. The optimal composition of cement, sand, milled flour, and water that produces the best compressive strength based on the response factor and SNR is 0.9 kg: 0.5 kg: 0.4 kg: 0.3 kg, respectively. The most influential factors on the compressive strength of concrete roof tiles are the amount of sand and the number of flour mills. Taguchi methods ; Roof ; Compressive strength ; Tile ; Cement ; Environmental science ; Materials science ; Geotechnical engineering ; Composite material ; Structural engineering ; Engineering Material Selection and Properties ; Geotechnical and construction materials studies ; Construction Engineering and Safety
Authors: J. Tittel | Fabian Knechtel | Evelyn Ploetz
Source: Advanced Functional Materials (article, 2023-12-13)
Field: Chemistry — Inorganic Chemistry

Conquering Metal–Organic Frameworks by Raman Scattering Techniques Abstract Metal–organic frameworks (MOFs) have emerged as a versatile class of materials. Understanding the structural and chemical properties of MOFs is essential for tailoring their performance to suit specific applications. In recent years, Raman spectroscopy, a non‐destructive vibrational spectroscopic technique, has evolved into a powerful tool for characterizing MOFs. Beyond spontaneous Raman scattering, numerous advanced Raman techniques have been developed to amplify the inherently weak Raman signal. These innovations have significantly enhanced the spatial resolution, frame rate, and sensitivity of Raman imaging, thereby opening up new possibilities for applications, such as label‐free sensing. This tutorial work is designed to demystify the fundamental theory and instrumentation of Raman spectroscopy, elucidating the differences between commonly used Raman techniques. Particular emphasis is placed on their advantages and limitations when applied to MOF materials. Furthermore, this work showcases how Raman techniques are employed in studying MOF systems to address societal needs and explore future directions. Ultimately, this review emphasizes the crucial role of Raman spectroscopy and the development of novel Raman‐based in situ techniques to conquer the field of MOFs. Raman spectroscopy ; Raman scattering ; Materials science ; Nanotechnology ; Characterization (materials science) ; Optics ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Nanomaterials in Catalysis
Authors: Jing‐Hong Li | Jun-Xian Chen | Rui‐Biao Lin | Xiao‐Ming Chen
Source: Materials Chemistry Frontiers (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Recent progress in metal–organic frameworks for the separation of gaseous hydrocarbons This review highlights recent research progress in MOF materials for the important separation of gaseous hydrocarbons, especially for ethylene, propylene, and butadiene. The uniqueness of MOFs to achieve those progress is also outlined. Ethylene ; Materials science ; Metal-organic framework ; Separation (statistics) ; Nanotechnology ; Chemistry ; Organic chemistry ; Catalysis ; Computer science ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Membrane Separation and Gas Transport