jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Leheng Zhang | Yizhou Hu | Sirui Gong | Zhenlong Wang
Source: Materials (article, 2024-05-11)
Field: Materials Science — Materials Chemistry

Experimental Study on the Comparison between Network Microstructure Titanium Matrix Composites and Ti6Al4V on EDM Milling Network microstructure titanium matrix composites (NMTMCs), featuring Ti6Al4V as the matrix and network-distributed TiB whiskers (TiBw) as reinforcement, exhibit remarkable potential for diverse applications due to their superior physical properties. Due to the difficulty in machining titanium matrix composites, electrical discharge machining (EDM) stands as one of the preferred machining techniques for NMTMCs. Nevertheless, the compromised surface quality and the recast layer significantly impact the performance of the workpiece machined by EDM. Therefore, for the purpose of enhancing the surface quality and restraining the defects of NMTMCs, this study conducted comparative EDM milling experiments between NMTMCs and Ti6Al4V to analyze the effects of discharge capacitance, charging current, and pulse interval on the surface roughness, recast layer thickness, recast layer uniformity, and surface microcrack density of both materials. The results indicated that machining energy significantly influences workpiece surface quality. Furthermore, comparative experiments exploring the influence of network reinforcement on EDM milling revealed that NMTMCs have a higher melting point, leading to an accumulation phenomenon in low-energy machining where the reinforcement could not be completely removed. The residual reinforcement in the recasting layer had an adsorption effect on molten metal affecting the thermal conductivity and uniformity within the recasting layer. Finally, specific guidelines are put forward for optimizing the material's surface roughness, recast layer thickness, and uniformity, along with minimizing microcrack density, which attain a processing effect that features a roughness of Ra 0.9 μm, an average recast layer thickness of 6 μm with a range of 8 μm, and a surface microcrack density of 0.08 μm<sup>-1</sup>. Microstructure ; Materials science ; Titanium alloy ; Composite material ; Titanium ; Matrix (chemical analysis) ; Metallurgy ; Alloy Titanium Alloys Microstructure and Properties ; Advanced Machining and Optimization Techniques ; Advanced materials and composites
Authors: Qian‐Qian Yang | Shuai‐Jiang Liu | Wei Huang | Cheng Peng | Bo Han
Source: Research (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Exploring Protein Bioconjugation: A Redox-Based Strategy for Tryptophan Targeting Amino acid bioconjugation technology has emerged as a pivotal tool for linking small-molecule fragments with proteins, antibodies, and even cells. The study in <i>Nature</i> by Chang and Toste introduces a redox-based strategy for tryptophan bioconjugation, employing <i>N</i>-sulfonyloxaziridines as oxidative cyclization reagents, demonstrating high efficiency comparable to traditional click reactions. Meanwhile, this tool provides feasible methods for investigating the mechanisms underlying functional tryptophan-related biochemical processes, paving the way for protein function exploration, activity-based proteomics for functional amino acid identification and characterization, and even the design of covalent inhibitors. Bioconjugation ; Redox ; Tryptophan ; Chemistry ; Biochemistry ; Combinatorial chemistry ; Amino acid ; Organic chemistry Click Chemistry and Applications ; Monoclonal and Polyclonal Antibodies Research ; Ubiquitin and proteasome pathways
Authors: Huat Thart Chiang | Kiran Vaddi | Lilo D. Pozzo
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-driven exploration of silver nanoplate formation in multidimensional chemical design spaces We present an autonomous data-driven framework that iteratively explores the experimental design space of silver nanoparticle synthesis to obtain control over the formation of a desired morphology and size. Space (punctuation) ; Nanotechnology ; Data-driven ; Silver nanoparticle ; Computer science ; Nanoparticle ; Control (management) ; Materials science ; Process engineering ; Biological system ; Biochemical engineering ; Engineering ; Artificial intelligence Gold and Silver Nanoparticles Synthesis and Applications ; Nanoparticles: synthesis and applications ; Nanomaterials for catalytic reactions
Authors: Weijun Liu | Yuanqing Wan | Haoxiang Yu | Quan Yuan | Tianyue Li | Fang Xue | Shuming Wang
Source: Journal of Optics (article, 2024-06-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Miniature snapshot mid-infrared spectrometer based on metal-insulator-metal metasurface Abstract Metasurfaces showcase the performance of light field manipulation at the subwavelength scale, generating tremendous applications in the field of optical imaging and sensing, especially in spectroscopic detection. Here, we demonstrate a spectral detector comprising metal-insulator-metal composite structures working in the mid-infrared band, which can effectively collect and restore target spectral characteristics in the mid-infrared band with a trained reconstruction algorithm. The proposed device consists of snapshot multichannel detection and spectral reconstruction, showing an average spectral reconstruction accuracy approaching 80% of the system. Moreover, we discuss the feasibility of applying this structural design to a miniature spectrometer over a wider infrared wavelength range by proposing a feasible design strategy. Our results provide a novel approach for low-cost and portable mid-infrared spectroscopic detection in ultracompact mid-infrared spectral imaging and sensing elements. Optics ; Snapshot (computer storage) ; Spectrometer ; Materials science ; Metal-insulator-metal ; Metal ; Physics ; Computer science Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Optical Wireless Communication Technologies
Authors: Xinyi Li | Yinghuan Fu | Nannan Wei | Ru‐Jia Yu | Huma Aslam Bhatti | Limin Zhang | Feng Yan | Fan Xia | Andrew G. Ewing | Yi‐Tao Long | Yi‐Lun Ying
Source: Angewandte Chemie (article, 2024-02-27)
Field: Chemistry — Electrochemistry

Emerging Data Processing Methods for Single‐Entity Electrochemistry Abstract Single‐entity electrochemistry is a powerful tool that enables the study of electrochemical processes at interfaces and provides insights into the intrinsic chemical and structural heterogeneities of individual entities. Signal processing is a critical aspect of single‐entity electrochemical measurements and can be used for data recognition, classification, and interpretation. In this review, we summarize the recent five‐year advances in signal processing techniques for single‐entity electrochemistry and highlight their importance in obtaining high‐quality data and extracting effective features from electrochemical signals, which are generally applicable in single‐entity electrochemistry. Moreover, we shed light on electrochemical noise analysis to obtain single‐molecule frequency fingerprint spectra that can provide rich information about the ion networks at the interface. By incorporating advanced data analysis tools and artificial intelligence algorithms, single‐entity electrochemical measurements would revolutionize the field of single‐entity analysis, leading to new fundamental discoveries. Electrochemistry ; Computer science ; Nanotechnology ; Data science ; Chemistry ; Materials science ; Electrode Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Advanced Chemical Sensor Technologies
Authors: Benoît Creton | Nathalie Brassart | Amandine Herbaut | Mickaël Matrat
Source: Energy & Fuels (article, 2024-08-05)
Field: Chemistry — Spectroscopy

Numerical Approaches to Determine Cetane Number of Hydrocarbons and Oxygenated Compounds, Mixtures, and their Blends In the present work, we report the development and use of models to predict the cetane number of hydrocarbons and oxygenated compounds, mixtures, and their blends. The study is divided in three steps: (i) the prediction of pure compounds’ CN using ML-based approaches, (ii) the development and application of mixing rules, and (iii) the external validation of models on a set of real fuels. Experimental CN values for 658 pure compounds are collected from the literature and merged to obtain a consistent and comprehensive database. ML-based models are then trained on the database. A second database is built from the collection of 572 experimental CN values for mixtures. Existing and proposed mixing rules powered by either experimental CN or CN predicted using the ML-based models are then assessed on the basis of the second database. The new mixing rule involving the activity coefficients of mixtures’ components shows the best performance. Finally, the application of our predictive numerical approach to 27 real fuels demonstrates its accuracy and relevance, and that it could be further used for testing large numbers of samples. Cetane number ; Chemistry ; Organic chemistry ; Oxygenate ; Biodiesel Analytical Chemistry and Chromatography ; Phase Equilibria and Thermodynamics ; Chemical Thermodynamics and Molecular Structure
Authors: Tom E. de Vries | Evi van Eert | Lucas Weevers | Paul Tinnemans | Elias Vlieg | Hugo Meekes | R. De Gelder
Source: Crystal Growth & Design (article, 2024-06-03)
Field: Materials Science — Materials Chemistry

Optimizing Link Prediction for the CSD Cocrystal Network: A Demonstration Using Praziquantel The physicochemical properties of chemical compounds can be altered and optimized by cocrystallization with a suitable coformer. However, discovering suitable coformers is a difficult and expensive process. Link prediction is one of the several techniques developed to predict suitable new coformers computationally. Link prediction uses a network of known coformers extracted from, e.g., the Cambridge Structural Database (CSD) to predict new cocrystals. We have investigated link prediction methods and were able to improve the performance of these methods using a scoring function called “multi-steps resource allocation”. Further improvements were obtained by examining the local structure of the network to remove imperfections and by using an algorithm previously designed by us to bipartise the network, thus removing imperfections on a global scale. By repeatedly predicting and synthesizing new cocrystals and adding them to the network to predict more new cocrystals, we obtain more and better predictions, but saturation of the local network eventually leads to diminishing returns. We demonstrate this for praziquantel (PZQ), a drug used to treat schistosomiasis. We discovered 11 new cocrystals for this compound, one of which is a racemic conglomerate that could be used to improve the medical efficacy of PZQ, and present 6 new cocrystal structures. Cocrystal ; Praziquantel ; Link (geometry) ; Chemistry ; Computer science ; Artificial intelligence ; Molecule ; Medicine ; Computer network ; Immunology ; Hydrogen bond ; Organic chemistry Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Jiajun Ma | Shengtao Dai | Zongwei Guo | Lei Shang | Yuhui Ao | Lin Jin
Source: Polymer Composites (article, 2024-09-30)
Field: Materials Science — Materials Chemistry

Impact of graphene oxide lateral sizes on the mechanical and thermal properties of carbon fiber composites Abstract The impact of carbon fiber (CF) composites sizing with graphene oxide (GO) to form brick‐and‐mortar structures on their mechanical properties and thermal conductivity has not been closely examined. This study systematically investigates the effect of GO sheet size on the interfacial properties and thermal conductivity of CF composites. Three sizes of GO—small (0.11 μm), medium (0.33 μm), and large (0.97 μm)—were used to modify CF surfaces via layer‐by‐layer self‐assembly, forming the brick‐and‐mortar structures. The mechanical properties were assessed through flexural tests, interlaminar shear strength (ILSS) tests and tensile tests, while thermal conductivity was measured using a thermal constant analyzer. The results revealed that medium‐sized GO (GO‐M) significantly enhanced the interfacial strength and toughness of the composites. This improvement is attributed to the orderly arrangement of GO‐M on the CF surface, which enhances stress transfer between the fiber and the matrix. In contrast, small‐sized GO (GO‐S) tended to scatter, resulting in less effective reinforcement, while large‐sized GO (GO‐L) exhibited stacking and agglomeration, limiting its reinforcement effectiveness. However, GO‐L provided the highest thermal conductivity due to the formation of continuous heat conduction pathways. These findings suggest that GO‐M offers a balanced improvement in mechanical properties, whereas GO‐L is more suitable for applications requiring high thermal conductivity. This work underscores the importance of selecting the appropriate GO sheet size to optimize the interfacial properties and thermal conductivity of CF composites, which is crucial for enhancing their performance in high‐end engineering applications, such as aerospace, automotive, and marine industries. Highlights Modification of CF composites by GO‐M sheets significantly improved toughness and strength. GO‐L sheets formed an effective heat transfer channel with the highest thermal conductivity. The study emphasized the critical role of selecting the appropriate GO sheet size to optimize the mechanical and thermal properties of CF composites. Materials science ; Composite material ; Graphene ; Oxide ; Thermal ; Fiber ; Carbon fiber composite ; Carbon fibers ; Composite number ; Nanotechnology Graphene research and applications ; Fiber-reinforced polymer composites ; Carbon Nanotubes in Composites
Authors: Pattipong Wisanpitayakorn | Sitanan Sartyoungkul | Alongkorn Kurilung | Yongyut Sirivatanauksorn | Wonnop Visessanguan | Nuankanya Sathirapongsasuti | Sakda Khoomrung
Source: Journal of Chemical Information and Modeling (article, 2024-02-23)
Field: Chemistry — Spectroscopy

Accurate Prediction of Ion Mobility Collision Cross-Section Using Ion’s Polarizability and Molecular Mass with Limited Data The rotationally averaged collision cross-section (CCS) determined by ion mobility-mass spectrometry (IM-MS) facilitates the identification of various biomolecules. Although machine learning (ML) models have recently emerged as a highly accurate approach for predicting CCS values, they rely on large data sets from various instruments, calibrants, and setups, which can introduce additional errors. In this study, we identified and validated that ion's polarizability and mass-to-charge ratio (<i>m</i>/<i>z</i>) have the most significant predictive power for traveling-wave IM CCS values in relation to other physicochemical properties of ions. Constructed solely based on these two physicochemical properties, our CCS prediction approach demonstrated high accuracy (mean relative error of <3.0%) even when trained with limited data (15 CCS values). Given its ability to excel with limited data, our approach harbors immense potential for constructing a precisely predicted CCS database tailored to each distinct experimental setup. A Python script for CCS prediction using our approach is freely available at https://github.com/MSBSiriraj/SVR_CCSPrediction under the GNU General Public License (GPL) version 3. Polarizability ; Python (programming language) ; Collision ; Ion ; Computer science ; Mass spectrometry ; Ion-mobility spectrometry ; Chemistry ; Analytical Chemistry (journal) ; Data mining ; Computational physics ; Physics ; Molecule ; Chromatography ; Programming language Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Yuliang Yang | Cui Cen | Lijun Kan | Qi Zhao | Zhongming Huang | Shengliang Li
Source: Interdisciplinary materials (article, 2024-10-07)
Field: Materials Science — Materials Chemistry

Organic probes for three‐photon fluorescence imaging in NIR‐II window: Design, applications, and perspectives Abstract Three‐photon fluorescence (3PF) imaging is an emerging technology for imaging deep‐tissue submicroscopic structures by nonlinearly redshifting the excitation wavelength to the second near‐infrared (NIR‐II) window; thus, this approach has great advantages, including deep penetration depth, good spatial resolution, low background, and a high signal‐to‐noise ratio. 3PF imaging has been demonstrated to be a powerful tool for noninvasively visualizing all kinds of deep tissues in recent years. Benefiting from excellent biosecurity and structural controllability, the development of organic 3PF probes is highly important for advancing 3PF imaging in vivo. However, there is no summary of the generalizability of the design and recent progress in organic 3PF probes. Herein, this review introduces the fundamental principle of 3PF imaging and highlights the advantages of 3PF bioimaging. The molecular design of these organic 3PF probes is also summarized based on relative optical indices. Furthermore, different 3PF imaging application scenarios are listed in detail. In the end, the main challenges, significance of probe exploitation, and prospective orientation of organic probes for precise 3PF imaging are proposed and discussed for promoting future applications and clinical translation. Window (computing) ; Fluorescence ; Materials science ; Optics ; Computer science ; Physics ; World Wide Web Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics ; Analytical Chemistry and Sensors
Authors: Eemeli A. Eronen | Anton Vladyka | Florent Gerbon | Christoph J. Sahle | Johannes Niskanen
Source: Journal of Physics Communications (article, 2024-01-17)
Field: Materials Science — Materials Chemistry

Information bottleneck in peptide conformation determination by x-ray absorption spectroscopy Abstract We apply a recently developed technique utilizing machine learning for statistical analysis of computational nitrogen K-edge spectra of aqueous triglycine. This method, the emulator-based component analysis, identifies spectrally relevant structural degrees of freedom from a data set filtering irrelevant ones out. Thus tremendous reduction in the dimensionality of the ill-posed nonlinear inverse problem of spectrum interpretation is achieved. Structural and spectral variation across the sampled phase space is notable. Using these data, we train a neural network to predict the intensities of spectral regions of interest from the structure. These regions are defined by the temperature-difference profile of the simulated spectra, and the analysis yields a structural interpretation for their behavior. Even though the utilized local many-body tensor representation implicitly encodes the secondary structure of the peptide, our approach proves that this information is irrecoverable from the spectra. A hard x-ray Raman scattering experiment confirms the overall sensibility of the simulated spectra, but the predicted temperature-dependent effects therein remain beyond the achieved statistical confidence level. Spectroscopy ; Bottleneck ; Absorption (acoustics) ; Peptide ; Absorption spectroscopy ; Information bottleneck method ; Crystallography ; X-ray ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Optics ; Computer science ; Physics ; Artificial intelligence ; Biochemistry ; Chromatography ; Astronomy Enzyme Structure and Function ; Machine Learning in Materials Science ; Protein Structure and Dynamics
Authors: Gabriele Greco | Benjamin Schmuck | Fredrik Bäcklund | Günter Reiter | Anna Rising
Source: ACS Applied Polymer Materials (article, 2024-11-20)
Field: Materials Science — Biomaterials

Post-spin Stretch Improves Mechanical Properties, Reduces Necking, and Reverts Effects of Aging in Biomimetic Artificial Spider Silk Fibers Recent biotechnological advancements in protein production and development of biomimetic spinning procedures make artificial spider silk a promising alternative to petroleum-based fibers. To enhance the competitiveness of artificial silk in terms of mechanical properties, refining the spinning techniques is imperative. One potential strategy involves the integration of post-spin stretching, known to improve fiber strength and stiffness while potentially offering additional advantages. Here, we demonstrate that post-spin stretching not only enhances the mechanical properties of artificial silk fibers but also restores a higher and more uniform alignment of the protein chains, leading to a higher fiber toughness. Additionally, fiber properties may be reduced by processes, such as aging, that cause increased network entropy. Post-spin stretching was found to partially restore the initial properties of fibers exposed aging. Finally, we propose to use the degree of necking as a simple measure of fiber quality in the development of spinning procedures for biobased fibers. Spinning ; Necking ; Materials science ; Toughness ; SILK ; Spider silk ; Fiber ; Composite material ; Stiffness ; Nanotechnology Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications ; Surface Modification and Superhydrophobicity
Authors: Shahla Hosseini Bai | Mahshid Tootoonchy | Wiebke Kämper | Iman Tahmasbian | Michael B. Farrar | Helen Boldingh | Trisha Pereira | Hannah Jonson | Joel Nichols | Helen M. Wallace | Stephen J. Trueman
Source: Remote Sensing (article, 2024-09-12)
Field: Chemistry — Analytical Chemistry

Predicting Carbohydrate Concentrations in Avocado and Macadamia Leaves Using Hyperspectral Imaging with Partial Least Squares Regressions and Artificial Neural Networks Carbohydrate levels are important regulators of the growth and yield of tree crops. Current methods for measuring foliar carbohydrate concentrations are time consuming and laborious, but rapid imaging technologies have emerged with the potential to improve the effectiveness of tree nutrient management. Carbohydrate concentrations were predicted using hyperspectral imaging (400–1000 nm) of leaves of the evergreen tree crops, avocado, and macadamia. Models were developed using partial least squares regression (PLSR) and artificial neural network (ANN) algorithms to predict carbohydrate concentrations. PLSR models had R2 values of 0.51, 0.82, 0.86, and 0.85, and ANN models had R2 values of 0.83, 0.83, 0.78, and 0.86, in predicting starch, sucrose, glucose, and fructose concentrations, respectively, in avocado leaves. PLSR models had R2 values of 0.60, 0.64, 0.91, and 0.95, and ANN models had R2 values of 0.67, 0.82, 0.98, and 0.98, in predicting the same concentrations, respectively, in macadamia leaves. ANN only outperformed PLSR when predicting starch concentrations in avocado leaves and sucrose concentrations in macadamia leaves. Performance differences were possibly associated with nonlinear relationships between carbohydrate concentrations and reflectance values. This study demonstrates that PLSR and ANN models perform well in predicting carbohydrate concentrations in evergreen tree-crop leaves. Partial least squares regression ; Hyperspectral imaging ; Artificial neural network ; Biological system ; Environmental science ; Mathematics ; Artificial intelligence ; Computer science ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement