Engineering topics
Wang, Yifan
Publications and source records attributed to Wang, Yifan.
Discovery of multimechanisms of screw dislocation interaction in bcc iron from open-ended saddle point searches
Dislocation motion and interactions determine mechanical properties in body-centered cubic (bcc) metallic materials. However, studying mechanisms for the screw dislocation interaction is fundamentally challenging since many underlying processes involve mesotimescales and atomistic resolution, currently inaccessible by either experimental techniques or continuum theoretical methods. Here, in this paper, we develop a computational capability based on self-evolving atomistic kinetic Monte Carlo (SEAKMC) to sample the critical events and saddle point energies related to screw dislocations and their junctions. The method is first validated by calculating the stress dependence of Peierls barriers and formation energies of kink pairs and cross-slip kink pairs on a single screw dislocation in bcc iron. Then the method is applied to a binary junction of a pair of intersecting screw dislocations, the structure of which is crucial for low-temperature plastic deformation. We identify three important mechanisms: coplanar cross-slipping, jog-pinning, and a previously unknown unzipping mechanism during the evolution of the binary junction. The mechanisms are then further validated using classical molecular dynamics simulations. The computational capability developed in this paper provides an effective tool to evaluate screw dislocation related thermally activated events in complex stress conditions. The mechanisms discovered in this paper provide critical insights into temperature dependence of the anomalous slip, a breakdown of the Schmidt law, during the plastic deformation in bcc iron and can be generalized to other bcc metals.
THESEUS: An Optimal Design and Downselection Framework for Energy Storage Technologies
The talk presents development of a software framework for optimal design and downselction of energy storage technologies.
Quasi‐Homojunction Organic Nonfullerene Photovoltaics Featuring Fundamentals Distinct from Bulk Heterojunctions
Abstract In contrast to classical bulk heterojunction (BHJ) in organic solar cells (OSCs), the quasi‐homojunction (QHJ) with extremely low donor content (≤10 wt.%) is unusual and generally yields much lower device efficiency. Here, representative polymer donors and nonfullerene acceptors are selected to fabricate QHJ OSCs, and a complete picture for the operation mechanisms of high‐efficiency QHJ devices is illustrated. PTB7‐Th:Y6 QHJ devices at donor:acceptor (D:A) ratios of 1:8 or 1:20 can achieve 95% or 64% of the efficiency obtained from its BHJ counterpart at the optimal D:A ratio of 1:1.2, respectively, whereas QHJ devices with other donors or acceptors suffer from rapid roll‐off of efficiency when the donors are diluted. Through device physics and photophysics analyses, it is observed that a large portion of free charges can be intrinsically generated in the neat Y6 domains rather than at the D/A interface. Y6 also serves as an ambipolar transport channel, so that hole transport as also mainly through Y6 phase. The key role of PTB7‐Th is primarily to reduce charge recombination, likely assisted by enhancing quadrupolar fields within Y6 itself, rather than the previously thought principal roles of light absorption, exciton splitting, and hole transport.
The hitchhiker's guide to 4d $\mathcal{N}=2$ superconformal field theories
Superconformal field theory with \mathcal{N}=2 𝒩 = 2 supersymmetry in four dimensional spacetime provides a prime playground to study strongly coupled phenomena in quantum field theory. Its rigid structure ensures valuable analytic control over non-perturbative effects, yet the theory is still flexible enough to incorporate a large landscape of quantum systems. Here we aim to offer a guidebook to fundamental features of the 4d \mathcal{N}=2 𝒩 = 2 superconformal field theories and basic tools to construct them in string/M-/F-theory. The content is based on a series of lectures at the Quantum Field Theories and Geometry School in July 2020.
Optimal design and integration of decentralized electrochemical energy storage with renewables and fossil plants
Increasing renewable energy requires improving the electricity grid flexibility. Existing measures include power plant cycling and grid-level energy storage, but they incur high operational and investment costs. Using a systems modeling and optimization framework, we study the integration of electrochemical energy storage with individual power plants at various renewable penetration levels. Furthermore, our techno-economic analysis includes both Li-ion and NaS batteries to encompass different technology maturity levels. A California case-study indicates localized integration to be cost-effective for greater grid flexibility. Li-ion batteries can mitigate the residual demand fluctuations of small to medium-sized plants, while NaS batteries would be best-suited for larger storage with higher renewable penetration. Overall, the battery-enabled renewable integration could reduce the unmet grid demand by 75%, the renewable curtailment by 58%, and the CO 2 emission intensity by 16% while including the life cycle emissions of the battery and the renewable farm. Our scenario-based analysis also indicates that rather than replacing all fossil power plants, it is more economical to combine batteries and renewables with individual fossil plants to achieve a clean energy grid.
LigninGraphs: lignin structure determination with multiscale graph modeling
Lignin is an aromatic biopolymer found in ubiquitous sources of woody biomass. Designing and optimizing lignin valorization processes requires a fundamental understanding of lignin structures. Experimental characterization techniques, such as 2D-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra, could elucidate the global properties of the polymer molecules. Computer models could extend the resolution of experiments by representing structures at the molecular and atomistic scales. We introduce a graph-based multiscale modeling framework for lignin structure generation and visualization. The framework employs accelerated rejection-free polymerization and hierarchical Metropolis Monte Carlo optimization algorithms. We obtain structure libraries for various lignin feedstocks based on literature and new experimental NMR data for poplar wood, pinewood, and herbaceous lignin. The framework could guide researchers towards feasible lignin structures, efficient space exploration, and future kinetics modeling. Its software implementation in Python, LigninGraphs, is open-source and available on GitHub.
A Novel Anatomic Reconstruction for Posterior Tibialis Tendon in Treatment of Flexible Adult‐Acquired Flatfoot Deformity
Objective To present a novel approach for the anatomic reconstruction of the posterior tibialis tendon (PTT) in restoring plantar insertions and evaluate its efficiency in treating flexible adult‐acquired flatfoot deformity (AAFD) caused by PTT dysfunction. Methods For AAFD treatment, a novel PTT reconstruction method was presented. The current study involved 16 patients, including three men, and 13 women, from August 2017 to July 2019. The mean age was 43.2 ± 15.1 years (21–64 years). The innovative PTT repair method was used on all patients. The treatment involved performing a traditional Flexor Digitorum Longus (FDL) transfer in the navicular tuberosity and suturing the plantar insertions to FDL as tension was applied to tighten the plantar structures of the foot. The results were retrospectively analyzed. The clinical outcome was assessed using the pain visual analogue scale (VAS), the satisfaction VAS, and the American Orthopedic Foot and Ankle Society ankle–hindfoot scale (AOFAS‐AH). Isokinetic testing was performed using a dynamometer at 60°/s and 120°/s for inversion/eversion and plantarflexion/dorsiflexion, respectively, to determine the mean peak torque. Radiographic measurements were employed to assess the outcomes. Results Bone surgeries combined with the modified anatomic PTT reconstruction were performed on patients with medializing calcaneal osteotomy in 12 (75%) patients and subtalar joint fusion in four (25%) patients. The branch linking to the plantar insertions was detected in every case, with an average width of 3.5 ± 0.8 mm (3.1–4.3 mm). All patients were followed up for the mean of 16.8 ± 1.8 months (range, 15–20 months). The average postoperative functional scores, including pain VAS, satisfaction VAS, total AOFAS‐AH, and all AOFAS‐AH sub‐scales, steadily improved during the follow‐up. In the last follow‐up, isokinetic testing revealed no loss of plantarflexion strength ( p = 0.350 and 0.098) and significant improvement in the inversion strength ( p = 0.007 and 0.008) in the operated ankles at 60°/s and 120°/s. Radiographic outcomes, particularly the talar head uncovering, improved significantly after more than a year ( p < 0.001 for all). Conclusions The novel technique for PTT reconstruction in restoring the plantar insertions serves as an effective procedure in treating AAFD caused by PTT dysfunction in terms of delivering a consistent improvement in ankle inversion strength, medial longitudinal arch restoring, and satisfactory clinical outcomes.
Programmable heating and quenching for efficient thermochemical synthesis
Conventional thermochemical syntheses by continuous heating under near-equilibrium conditions face critical challenges in improving the synthesis rate, selectivity, catalyst stability and energy efficiency, owing to the lack of temporal control over the reaction temperature and time, and thus the reaction pathways. As an alternative, we present a non-equilibrium, continuous synthesis technique that uses pulsed heating and quenching (for example, 0.02 s on, 1.08 s off) using a programmable electric current to rapidly switch the reaction between high (for example, up to 2,400 K) and low temperatures. The rapid quenching ensures high selectivity and good catalyst stability, as well as lowers the average temperature to reduce the energy cost. Using CH4 pyrolysis as a model reaction, our programmable heating and quenching technique leads to high selectivity to value-added C2 products (>75% versus <35% by the conventional non-catalytic method and versus <60% by most conventional methods using optimized catalysts). Our technique can be extended to a range of thermochemical reactions, such as NH 3 synthesis, for which we achieve a stable and high synthesis rate of about 6,000 μmol g Fe –1 h –1 at ambient pressure for >100 h using a non-optimized catalyst. Furthermore, this study establishes a new model towards highly efficient non-equilibrium thermochemical synthesis.
Insights into Supported Subnanometer Catalysts Exposed to CO via Machine-Learning-Enabled Multiscale Modeling
Subnanometer catalysts offer high noble metal utilization and superior performance for several reactions. However, understanding their structures and properties on an atomic scale under working conditions is challenging due to the large configurational space. Here, we introduce an efficient multiscale framework to predict their stability exposed to an adsorbate. The framework integrates a comprehensive toolset including density functional theory (DFT) calculations, cluster expansion, machine learning, and structure optimization. The end-to-end machine-learning workflow guides DFT data generation and enables significant computational acceleration. We demonstrate the approach for CO-adsorbed Pdn (n = 1–55) clusters on CeO 2 (111). Simulation results reveal that CO can facilitate restructuring by stabilizing smaller planar structures and bilayer structures of specific intermediate sizes, consistent with experimental reports. Metal–support interactions, preferential CO adsorption, and metal nuclearity and structure control catalyst stability. As a result, the framework allows automatic discovery of stable catalyst structures and a systematic strategy to exploit properties in the subnanometer scale.
High-Content Clonogenic Survival Screen to Identify Chemoradiation Sensitizers
The combination of cytotoxic chemotherapy with radiation therapy (CRT) has resulted in significant improvements in clinical outcomes for patients with many locally advanced unresectable cancers. Only a small proportion of patients achieve pathologic complete responses to CRT; combination of CRT with targeted agents offers the promise of further improving treatment responses. However, numerous clinical trials have failed to show an improvement in clinical outcomes with the addition of targeted agents. To increase the accessibility of our screening method and accelerate the pace at which novel combinations with CRT are identified and incorporated into standard practices for treatments, we report details on screening method optimization, data generation, and downstream data analysis.
NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering
Automation and optimization of chemical systems require well-informed decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. Here we introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian optimization algorithms and supports both automated an d human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub
Multiobjective Dynamic Optimization for Optimal Load-Following of Natural Gas Combined Cycle Power Plants under Stress Constraints
With the increasing penetration of intermittent renewable energy sources into the electric grid, there is an associated need for conventional thermal power plants that are designed to operate at base-load conditions to cycle their load rapidly and frequently and operate under low-load conditions. Fast load-following operations lead to plant efficiency loss and the reduction of equipment life. A high-fidelity dynamic model of a natural gas combined cycle power plant, with rigorous equipment level submodels, is developed to capture the plant transient performance and the thermomechanical stress evolution at the high-pressure drum is computed to assess the drum life consumption. Stress evolution is modeled for the location of the edge at the drum/downcomer junction that experiences high circumferential stress amplitude during the fast load-following operation, leading to higher fatigue damage than locations considered under existing design standards. A dynamic optimization problem is solved for optimal load-following operation. Depending on the value of the stress constraint and the desired ramp rate, satisfying the desired stress constraint may be infeasible without relaxing the ramp rate. A multiobjective dynamic optimization problem is solved using a lexicographic approach that minimizes ramp rate relaxation and maximizes efficiency while satisfying stress constraints, avoiding spraying to saturation, and maintaining main steam and reheat steam temperature within bounds. It was observed that the optimal ramp rate can be highly nonlinear as opposed to the industry-standard linear ramp rate. Nonlinear optimal ramp-rate profiles not only help to avoid stress constraints that may be unavoidable by using the linear profile but also result in higher efficiency than the linear profile. Furthermore, the study shows that there is a strong tradeoff between the relaxation in the plant ramp rate and the time-average thermal efficiency.
PTEN protects kidney against acute kidney injury by alleviating apoptosis and promoting autophagy via regulating HIF1-α and mTOR through PI3K/Akt pathway
Highlights: • PTEN protects kidney against acute kidney injury. • The effect of PTEN works by alleviating apoptosis and promoting autophagy via regulating HIF1-α and mTOR by PI3K/Akt pathway. Phosphatase and tensin homolog (PTEN) deleted on human chromosome 10 is a tumor suppressor with bispecific phosphatase activity, which is often involved in the study of energy metabolism and tumorigenesis. PTEN is recently reported to participate in the process of acute injury. However, the mechanism of PTEN in Ischemia-Reperfusion Injury (IRI) has not yet been clearly elucidated. In this study, mice with bilateral renal artery ischemia-reperfusion and HK-2 cells with hypoxia/reoxygenation (H/R) were used as acute kidney injury models. We demonstrated that PTEN was downregulated in IRI-induced kidney as well as in H/R-induced HK-2 cells. By silencing and overexpressing PTEN with si-PTEN RNA and PHBLV-CMV-PTEN-flag lentivirus before H/R, we found that PTEN protected HK-2 cells against H/R-induced injury reflected by the change in cell activity and the release of LDH. Furthermore, we inhibited HIF1-α with PX-478 and inactivated mTOR with Rapamycin before the silence of PTEN in H/R model. Our data indicated that the renoprotective effect of PTEN worked via PI3K/Akt/mTOR pathway and PI3K/Akt/HIF1-α pathway, hence alleviating apoptosis and improving autophagy respectively. Our findings provide valuable insights into the molecular mechanism underlying renoprotection of PTEN on autophagy and apoptosis induced by renal IRI, which offers a novel therapeutic target for the treatment of AKI.
Real-time dynamics and structures of supported subnanometer catalysts via multiscale simulations
Understanding the performance of subnanometer catalysts and how catalyst treatment and exposure to spectroscopic probe molecules change the structure requires accurate structure determination under working conditions. Experiments lack simultaneous temporal and spatial resolution and could alter the structure, and similar challenges hinder first-principles calculations from answering these questions. Here, we introduce a multiscale modeling framework to follow the evolution of subnanometer clusters at experimentally relevant time scales. We demonstrate its feasibility on Pd adsorbed on CeO 2 (111) at various catalyst loadings, temperatures, and exposures to CO. We show that sintering occurs in seconds even at room temperature and is mainly driven by free energy reduction. It leads to a kinetically (far from equilibrium) frozen ensemble of quasi-two-dimensional structures that CO chemisorption and infrared experiments probe. CO adsorption makes structures flatter and smaller. High temperatures drive very rapid sintering toward larger, stable/metastable equilibrium structures, where CO induces secondary structure changes only.
Proving the 6d Cardy formula and matching global gravitational anomalies
A Cardy formula for 6d superconformal field theories (SCFTs) conjectured by Di Pietro and Komargodski in [1] governs the universal behavior of the supersymmetric partition function on S^1_\beta \times S^5 S β 1 × S 5 in the limit of small \beta β and fixed squashing of the S^5 S 5 . For a general 6d SCFT, we study its 5d effective action, which is dominated by the supersymmetric completions of perturbatively gauge-invariant Chern-Simons terms in the small \beta β limit. Explicitly evaluating these supersymmetric completions gives the precise squashing dependence in the Cardy formula. For SCFTs with a pure Higgs branch (also known as very Higgsable SCFTs), we determine the Chern-Simons levels by explicitly going onto the Higgs branch and integrating out the Kaluza-Klein modes of the 6d fields on S^1_\beta S β 1 . We then discuss tensor branch flows, where an apparent mismatch between the formula in [1] and the free field answer requires an additional contribution from BPS strings. This ``missing contribution’’ is further sharpened by the relation between the fractional part of the Chern-Simons levels and the (mixed) global gravitational anomalies of the 6d SCFT. We also comment on the Cardy formula for 4d \mathcal{N}=2 𝒩 = 2 SCFTs in relation to Higgs branch and Coulomb branch flows.
3d N=4 Bootstrap and Mirror Symmetry
We investigate the non-BPS realm of 3d {N} = 4 N = 4 superconformal field theory by uniting the non-perturbative methods of the conformal bootstrap and supersymmetric localization, and utilizing special features of 3d {N} = 4 N = 4 theories such as mirror symmetry and a protected sector described by topological quantum mechanics (TQM). Supersymmetric localization allows for the exact determination of the conformal and flavor central charges, and the latter can be fed into the mini-bootstrap of the TQM to solve for a subset of the OPE data. We examine the implications of the Z_2 Z 2 mirror action for the SCFT single- and mixed-branch crossing equations for the moment map operators, and apply numerical bootstrap to obtain universal constraints on OPE data for given flavor symmetry groups. A key ingredient in applying the bootstrap analysis is the determination of the mixed-branch superconformal blocks. Among other results, we show that the simplest known self-mirror theory with SU(2) \times SU(2) S U ( 2 ) × S U ( 2 ) flavor symmetry saturates our bootstrap bounds, which allows us to extract the non-BPS data and examine the self-mirror Z_2 Z 2 symmetry thereof.
A novel catalytic heme cofactor in SfmD with a single thioether bond and a bis -His ligand set revealed by a de novo crystal structural and spectroscopic study
The de novo crystal structure of SfmD reveals a novel c -type heme cofactor for promoting a monooxygenation reaction in the biosynthetic pathway of saframycin A.