Publications

The dependencies of weight gain of 9-12 Cr ferritic-martensitic steels in supercritical water on each of seven principal independent variables (temperature, oxygen concentration, flow rate, exposure time, and key chemical composition and surface condition of steels) have been predicted using a supervised artificial neural network (ANN). The relative significance of each independent variable was uncovered by fuzzy curve analysis, which ranks temperature and exposure time as the most important. The optimized ANN, not only satisfactorily represents the experimentally-known non-linear relationships between the corrosion characteristics of F-M steels and the key independent variables (demonstrating the effectiveness of this technique), but also predicts and reveals that the effects of oxygen concentration on the weight gains, to a certain degree, is influenced by the flow rate and temperature. Finally, according to the ANN predicted-results, departure of oxidation kinetics from the parabolic law, and basic cause of chromium content in steel substrate influencing the corrosion rate, and the synergetic effects of dissolved oxygen concentration, flow rate, and temperature, are discussed and analyzed.

Abstract Machine learning (ML) has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors (hGPCRs) in FDA-approved drugs, exhaustive in-distribution drug-target interaction (DTI) testing across all pairs of hGPCRs and known drugs is rare due to significant economic and technical challenges. This often leaves off-target effects unexplored, which poses a considerable risk to drug safety. In contrast to the traditional focus on out-of-distribution (OOD) exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks (CSNN) that leverages network homophily and training-free graph neural networks (GNNs) with Labels as Features (LaF). We show that CSNN’s ability to make accurate predictions strongly correlates with network homophily. Thus, LaFs strongly increase a ML model’s capacity to enhance in-distribution prediction accuracy, which we show by integrating labeled data during inference. We validate these advancements in a high-throughput yeast biosensing system (3773 DTIs, 539 compounds, 7 hGPCRs) to discover novel DTIs for FDA-approved drugs and to expand the general understanding of how to build reliable predictors to guide experimental verification.

The tooth enamel of vertebrates comprises a hyper-mineralized bioceramic, but is distinguished by an exceptional durability to resist impact and wear throughout the lifetime of organisms; however, enamels exhibit a low resistance to the initiation of large-scale cracks comparable to that of geological minerals based on fracture mechanics. Here we reveal that the tooth enamel, specifically from the giant panda, is capable of developing durability through counteracting the early stage of damage by partially recovering its innate geometry and structure at nano- to micro- length-scales autonomously. Such an attribute results essentially from the unique architecture of tooth enamel, specifically the vertical alignment of nano-scale mineral fibers and micro-scale prisms within a water-responsive organic-rich matrix, and can lead to a decrease in the dimension of indent damage in enamel introduced by indentation. Hydration plays an effective role in promoting the recovery process and improving the indentation fracture toughness of enamel (by ∼73%), at a minor cost of micro-hardness (by ∼5%), as compared to the dehydrated state. The nano-scale mechanisms that are responsible for the recovery deformation, specifically the reorientation and rearrangement of mineral fragments and the inter- and intra-prismatic sliding between constituents that are closely related to the viscoelasticity of organic matrix, are examined and analyzed with respect to the structure of tooth enamel. Our study sheds new light on the strategies underlying Nature’s design of durable ceramics which could be translated into man-made systems in developing high-performance ceramic materials. Statement of Significance Tooth enamel plays a critical role in the function of teeth by providing a hard surface layer to resist wear/impact throughout the lifetime of organisms; however, such enamel exhibits a remarkably low resistance to the initiation of large-scale cracks, of hundreds of micrometers or more, comparable to that of geological minerals. Here we reveal that tooth enamel, specifically that of the giant panda, is capable of partially recovering its geometry and structure to counteract the early stages of damage at nano- to micro-scale dimensions autonomously. Such an attribute results essentially from the architecture of enamel but is markedly enhanced by hydration. Our work discerns a series of mechanisms that lead to the deformation and recovery of enamel and identifies a unique source of durability in the enamel to accomplish this function. The ingenious design of tooth enamel may inspire the development of new durable ceramic materials in man-made systems.

CO2 valorization is aimed at converting waste CO2 to value-added products. While steady progress has been achieved through diverse catalytic strategies, including CO2 electrosynthesis, CO2 thermocatalysis, and biological CO2 fixation, each of these approaches have distinct limitations. Inorganic catalysts only enable synthesis beyond C2 and C3 products with poor selectivity and with a high energy requirement. Meanwhile, although biological organisms can selectively produce complex products from CO2, their slow autotrophic metabolism limits their industrial feasibility. Here, we present an abiotic approach leveraging electrochemical and thermochemical catalysis to complete the conversion of CO2 to life-sustaining carbohydrate sugars akin to photosynthesis. CO2 was electrochemically converted to glycolaldehyde and formaldehyde using copper nanoparticles and boron-doped diamond cathodes, respectively. CO2-derived glycolaldehyde then served as the key autocatalyst for the formose reaction, where glycolaldehyde and formaldehyde combined in the presence of an alkaline earth metal catalyst to form a variety of C4 - C8 sugars, including glucose. In turn, these sugars were used as a feedstock for fast-growing and genetically modifiable Escherichia coli. Altogether, we have assembled a platform that pushes the boundaries of product complexity achievable from CO2 conversion while demonstrating CO2 integration into life-sustaining sugars.

We present the final installment of an intensive 13 year study of variations of the optical continuum and broad Hβ emission line in the Seyfert 1 galaxy NGC 5548. The database consists of 1530 optical continuum measurements and 1248 Hβ measurements. The Hβ variations follow the continuum variations closely, with a typical time delay of about 20 days. However, a year-by-year analysis shows that the magnitude of emission-line time delay is correlated with the mean continuum flux. We argue that the data are consistent with the simple model prediction between the size of the broad-line region and the ionizing luminosity, r α L^1/2 _ion. Moreover, the apparently linear nature of the correlation between the Hβ response time and the nonstellar optical continuum F_opt arises as a consequence of the changing shape of the continuum as it varies, specifically F_opt α F ^.56 _UV

Foreign-object damage associated with the ingestion of debris into aircraft turbine engines can lead to a marked degradation in the high-cycle fatigue life of turbine components. This degradation is generally considered to be associated with the premature initiation of fatigue cracks at or near the damage sites; this is suspected to be due to, at least in part, the impact-induced residual stress state, which can be strongly tensile in these locations.