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"Yu, Chi-Hua"
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Main-chain engineering of polymer photocatalysts with hydrophilic non-conjugated segments for visible-light-driven hydrogen evolution
Photocatalytic water splitting is attracting considerable interest because it enables the conversion of solar energy into hydrogen for use as a zero-emission fuel or chemical feedstock. Herein, we present a universal approach for inserting hydrophilic non-conjugated segments into the main-chain of conjugated polymers to produce a series of discontinuously conjugated polymer photocatalysts. Water can effectively be brought into the interior through these hydrophilic non-conjugated segments, resulting in effective water/polymer interfaces inside the bulk discontinuously conjugated polymers in both thin-film and solution. Discontinuously conjugated polymer with 10 mol% hexaethylene glycol-based hydrophilic segments achieves an apparent quantum yield of 17.82% under 460 nm monochromatic light irradiation in solution and a hydrogen evolution rate of 16.8 mmol m
−2
h
−1
in thin-film. Molecular dynamics simulations show a trend similar to that in experiments, corroborating that main-chain engineering increases the possibility of a water/polymer interaction. By introducing non-conjugated hydrophilic segments, the effective conjugation length is not altered, allowing discontinuously conjugated polymers to remain efficient photocatalysis.
The introduction of hydrophilic segments into the main-chain of polymer photocatalysts allows water to efficiently enter the interior through these hydrophilic segments, and results in effective water/polymer interfaces for hydrogen evolution.
Journal Article
Deep learning model to predict fracture mechanisms of graphene
Understanding fracture is critical to the design of resilient nanomaterials. Molecular dynamics offers a way to study fracture at an atomistic level, but is computationally expensive with limitations of scalability. In this work, we build upon machine-learning approaches for predicting nanoscopic fracture mechanisms including crack instabilities and branching as a function of crystal orientation. We focus on a particular technologically relevant material system, graphene, and apply a deep learning method to the study of such nanomaterials and explore the parameter space necessary for calibrating machine-learning predictions to meaningful results. Our results validate the ability of deep learning methods to quantitatively capture graphene fracture behavior, including its fractal dimension as a function of crystal orientation, and provide promise toward the wider application of deep learning to materials design, opening the potential for other 2D materials.
Journal Article
A micromechanical study on the effects of precipitation on the mechanical properties of CoCrFeMnNi high entropy alloys with various annealing temperatures
The CoCrFeMnNi high entropy alloys remain an active field over a decade owing to its excellent mechanical properties. However, the application of CoCrFeMnNi is limited because of the relatively low tensile strength. Here we proposed a micromechanical model which adopted from the theory of dislocation density to investigate the strengthening mechanisms of precipitation of chromium-rich non-equiatomic CoCrFeMnNi alloy. The microstructures of CoCrFeMnNi were obtained directly from SEM-BSE images with different annealing temperatures. The proposed framework is validated by comparing simulations with experiments of uniaxial tensile tests on the CoCrFeMnNi alloys under different annealing temperatures. The stress–strain curves indicate that the precipitate has greater influence on post-yield hardening than the initial yielding strength. In addition, we identified that the particle distribution, controlled by the average size of the particle and the volume fraction of precipitation, can significantly enhance the strengthening effect. The numerical results indicate that HEAs with a precipitate distribution closer to a normal distribution and with smaller average size will tend to have higher strength and ductility.
Journal Article
Deep Learning Model to Predict Ice Crystal Growth
The demand for highly specific and complex materials has made the development of controllable manufacturing processes crucial. Among the numerous manufacturing methods, casting is important because it is economical and highly flexible regarding the geometry of manufactured parts. Since solidification is an important stage in the casting process that influences the properties of the final product, the development of a controllable solidification process using modeling methods is necessary to create superior structural properties. However, traditional modeling methods are computationally expensive and require sophisticated mathematical schemes. Therefore, a deep learning model is proposed to predict the morphology of the dendritic crystal growth solidification process, along with a reinforcement learning model to control the solidification process. By training the deep learning model with data generated using the phase field method, the solidification process can be successfully predicted. The crystal growth structures are designed to be altered by adjusting the degree of supercooling in the deep learning model while implementing reinforcement learning to control the dendritic arteries. This research opens new avenues for applying artificial intelligence to the optimization of casting processes, with the potential to utilize it in the processing of advanced materials and to improve the target properties of material design. This work investigates a design framework that integrates machine learning to control and optimizes dendrite structures. A ResNet‐U‐Net surrogate model is successfully developed and trained to predict the phase evolution of dendrite structures. Meanwhile, the deep deterministic policy gradient is implemented to manage simulation parameters, ultimately generating structures with desired properties.
Journal Article
Polyaniline-Supported Atomic-Level Pt and Pt-Au Clusters as Catalytic Electrodes in Propanol Oxidation
Noble metals are widely recognized for their ability to catalyze the electro-oxidation of organic compounds, with smaller particle sizes significantly enhancing electrocatalytic activity. In this study, catalytic electrodes decorated with atomic-level platinum and Pt-Au clusters were fabricated using cyclic atomic-metal electrodeposition. The interactions between the iminium (protonated imine) groups in emeraldine salt polyaniline (PANI) and metal chloride complexes in the electrolyte enabled precise control over the cluster size and composition. The electrocatalytic activity of these electrodes for propanol oxidation was systematically evaluated using cyclic voltammetry (CV). Notably, PANI electrodes decorated with odd-numbered atomic-level Pt clusters exhibited higher peak oxidation currents compared to even-numbered clusters, revealing a unique even–odd effect. For atomic-level Pt-Au clusters, the catalytic activity was significantly influenced by the sequence of Pt and Au deposition, with PANI-Au1Pt3 achieving the highest catalytic activity (35.34 mA/cm2). Bi-metallic clusters consistently outperformed mono-metallic clusters, and clusters containing only one Pt atom demonstrated superior catalytic activity. These findings provide valuable insights into the design of high-performance catalytic electrodes by leveraging atomic-level control of the cluster size, composition, and deposition sequence, paving the way for advanced applications in electrochemical sensors.
Journal Article
Discovering design principles of collagen molecular stability using a genetic algorithm, deep learning, and experimental validation
Collagen is the most abundant structural protein in humans, providing crucial mechanical properties, including high strength and toughness, in tissues. Collagen-based biomaterials are, therefore, used for tissue repair and regeneration. Utilizing collagen effectively during materials processing ex vivo and subsequent function in vivo requires stability over wide temperature ranges to avoid denaturation and loss of structure, measured as melting temperature (Tm). Although significant research has been conducted on understanding how collagen primary amino acid sequences correspond to Tm values, a robust framework to facilitate the design of collagen sequences with specific Tm remains a challenge. Here, we develop a general model using a genetic algorithm within a deep learning framework to design collagen sequences with specific Tm values. We report 1,000 de novo collagen sequences, and we show that we can efficiently use this model to generate collagen sequences and verify their Tm values using both experimental and computational methods. We find that the model accurately predicts Tm values within a few degrees centigrade. Further, using this model, we conduct a highthroughput study to identify the most frequently occurring collagen triplets that can be directly incorporated into collagen.We further discovered that the number of hydrogen bonds within collagen calculated with molecular dynamics (MD) is directly correlated to the experimental measurement of triple-helical quality. Ultimately, we see this work as a critical step to helping researchers develop collagen sequences with specific Tm values for intended materials manufacturing methods and biomedical applications, realizing a mechanistic materials by design paradigm.
Journal Article
Unveiling the nonclassicality within quasi-distribution representations through deep learning
To unequivocally distinguish genuine quantumness from classicality, a widely adopted approach focuses on the negative values of a quasi-distribution representation as compelling evidence of nonclassicality. Prominent examples include the dynamical process nonclassicality characterized by the canonical Hamiltonian ensemble representation (CHER) and the nonclassicality of quantum states characterized by the Wigner function. However, to construct a multivariate joint quasi-distribution function with negative values from experimental data is typically highly cumbersome. Here we propose a computational approach utilizing a deep generative model, processing three marginals, to construct the bivariate joint quasi-distribution functions. We first apply our model to tackle the challenging problem of the CHERs, which lacks universal solutions, rendering the problem ground-truth (GT) deficient. To overcome the GT deficiency of the CHER problem, we design optimal synthetic datasets to train our model. While trained with synthetic data, the physics-informed optimization enables our model to capture the detrimental effect of the thermal fluctuations on nonclassicality, which cannot be obtained from any analytical solutions. This underscores the reliability of our approach. This approach also allows us to predict the Wigner functions subject to thermal noises. Our model predicts the Wigner functions with a prominent accuracy by processing three marginals of probability distributions. Our approach also provides a significant reduction of the experimental efforts of constructing the Wigner functions of quantum states, giving rise to an efficient alternative way to realize the quantum state tomography.
Paper
Study of Clinical and Genetic Risk Factors for Aspirin-inducec Gastric Mucosal Injury
Background： Current knowledge about clinical and genetic risk factors for aspirin-induced gastric mucosal injury is not sufficient to prevent these gastric mucosal lesions. Methods： We recruited aspirin takers as the exposed group and healthy volunteers as the control group. The exposed group was categorized into two subgroups such as subgroup A as gastric mucosal injury diagnosed by gastroscopy, including erosion, ulcer or bleeding of the esophagus, stomach, or duodenum; subgroup B as no injury of the gastric mucosa was detected by gastroscopy. Clinical information was collected, and 53 single nucleotide polymorphisms were evaluated. Results： Among 385 participants, 234 were in the aspirin-exposed group. According to gastroscopy, 82 belonged to subgroup A, 91 belonged to subgroup B, and gastroscopic results of 61 participants were not available. Using the Chi-square test and logistic regression, we found that peptic ulcer history （odds ratio [OR] = 5.924, 95% confidence intervals [C/]： 2.115-16.592）, dual anti-platelet medication （OR = 3.443, 95% CI： 1.154-10.271 ）, current Helicobacterpylori infection （OR = 2.242, 95% CI： 1.032-4.870）, male gender （OR = 2.211, 95% CI： 1.027-4.760）, GG genotype ofrs2243086 （OR = 4.516, 95% CI： I. 180-17.278）, and AA genotype ofrs 1330344 （OR = 2.178, 95% CI： 1.016-4.669） were more frequent in subgroup A than subgroup B. In aspirin users who suffered from upper gastrointestinal bleeding, the frequency of the TT genotype ofrs2238631 and TT genotype ofrs2243100 was higher than in those without upper gastrointestinal bleeding. Conclusions： Peptic ulcer history, dual anti-platelet medication, tt. pylori current infection, and male gender were possible clinical risk factors for aspirin-induced gastric mucosal injury. GG genotype of rs2243086 and AA genotype of rs 1330344 were possible genetic risk factors. TT genotype ofrs2238631 and TT genotype ofrs2243100 may be risk factors for upper gastrointestinal bleeding in aspirin users.
Journal Article