Engineering topics
Publications and source records attributed to Zhang, Yu.
The present invention provides isolated polypeptides having xylanase activity and polynucleotides encoding the polypeptides. The invention also provides nucleic acid constructs, vectors, and host cells comprising the polynucleotides as well as methods of producing and using the polypeptides.
Localized surface plasmon resonances (LSPRs) have attracted much recent attention for their potential in promoting chemical reactions with light. However, the mechanism of LSPR-induced chemical reactions is still not clear, even for H 2 dissociation on metal nanoparticles. Here, we investigate the mechanism for photoinduced H 2 dissociation using a simple H 2 @Au 6 model. Our time-dependent density functional theory calculations indicate that the initial excitation is largely restricted to the metal cluster, involving intraband excitation that produces hot electrons (HEs). However, diabatization via overlapping orbitals reveals two types of nested electronic states, one involving excitations of the metallic electrons, namely, the HE states, and the other concerned with charge transfer (CT) to the adsorbate antibonding σ* orbital. Dissociation of H 2 thus takes place by transitions from the former to the latter. Quantum dynamics simulations on the diabatic CT states suggest rapid dissociation of H 2 , while no such dissociation occurs on diabatic HE states. Our research provides a clear physical picture of photoinduced H 2 dissociation on Au clusters, which has important implications in plasmonic facilitated photocatalysis.
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Phosphorus (P) loading to lakes is degrading the quality and usability of water globally. Accurate predictions of lake P dynamics are needed to understand whole-ecosystem P budgets, as well as the consequences of changing lake P concentrations for water quality. However, complex biophysical processes within lakes, along with limited observational data, challenge our capacity to reproduce short-term lake dynamics needed for water quality predictions, as well as long-term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling, process-guided machine learning (PGML), to produce a phosphorus budget for Lake Mendota (Wisconsin, USA) and to accurately predict epilimnetic phosphorus over a time range of days to decades. In our implementation of PGML, which we term a Process-Guided Recurrent Neural Network (PGRNN), we combine a process-based model for lake P with a recurrent neural network, and then constrain the predictions with ecological principles. We test independently the process-based model, the recurrent neural network, and the PGRNN to evaluate the overall approach. The process-based model accounted for most of the observed pattern in lake P; however it missed the long-term trend in lake P and had the worst performance in predicting winter and summer P in surface waters. The root mean square error (RMSE) for the process-based model, the recurrent neural network, and the PGRNN was 33.0 μg P L -1 , 22.7 μg P L -1 , and 20.7 μg P L -1 , respectively. All models performed better during summer, with RMSE values for the three models (same order) equal to 14.3 μg P L -1 , 10.9 μg P L -1 , and 10.7 μg P L -1 . Although the PGRNN had only marginally better RMSE during summer, it had lower bias and reproduced long-term decreases in lake P missed by the other two models. For all seasons and all years, the recurrent neural network had better predictions than process alone, with root mean square error (RMSE) of 23.8 μg P L -1 and 28.0 μg P L -1 , respectively. The output of PGRNN indicated that new processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve the process model. By using ecological knowledge, as well as the information content of complex data, PGML shows promise as a technique for accurate prediction in messy, real-world ecological dynamics, while providing valuable information that can improve our understanding of process.
Protection equipment are used to prevent damages to induction motor loads by isolating those from the power network in the event of severe faults. Modeling the response of induction motor loads and their protection is vital for power system planning and operation, especially in understanding system's response moments after a fault has occurred. This article proposes an optimization based framework to generate composite protection models for commercial building motor loads. Introducing a mathematical abstraction, the task of finding a suitable (simplified) model of the composite protection scheme is formulated as a nonlinear regression problem. Numerical examples are provided to illustrate the application of the framework.
Abstract Quantum spin systems such as magnetic insulators usually show magnetic order, but such classical states can give way to quantum liquids with exotic entanglement through two known mechanisms of frustration: geometric frustration in lattices with triangle motifs, and spin-orbit-coupling frustration in the exactly solvable quantum liquid of Kitaev’s honeycomb lattice. Here we present the experimental observation of a new kind of frustrated quantum liquid arising in an unlikely place: the magnetic insulator Ba 4 Ir 3 O 10 where Ir 3 O 12 trimers form an unfrustrated square lattice. The crystal structure shows no apparent spin chains. Experimentally we find a quantum liquid state persisting down to 0.2 K that is stabilized by strong antiferromagnetic interaction with Curie–Weiss temperature ranging from −766 to −169 K due to magnetic anisotropy. The anisotropy-averaged frustration parameter is 2000, seldom seen in iridates. Heat capacity and thermal conductivity are both linear at low temperatures, a familiar feature in metals but here in an insulator pointing to an exotic quantum liquid state; a mere 2% Sr substitution for Ba produces long-range order at 130 K and destroys the linear-T features. Although the Ir 4+ (5d 5 ) ions in Ba 4 Ir 3 O 10 appear to form Ir 3 O 12 trimers of face-sharing IrO 6 octahedra, we propose that intra-trimer exchange is reduced and the lattice recombines into an array of coupled 1D chains with additional spins. An extreme limit of decoupled 1D chains can explain most but not all of the striking experimental observations, indicating that the inter-chain coupling plays an important role in the frustration mechanism leading to this quantum liquid.
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Advances in chemical control of the photophysical properties of transition-metal complexes are revolutionizing a wide range of technologies, particularly photocatalysis and light-emitting diodes, but they rely heavily on molecules containing precious metals such as ruthenium and iridium. While the application of earth-abundant ‘early’ transition metals in photosensitizers is clearly advantageous, a detailed understanding of excited states with ligand-to-metal charge transfer (LMCT) character is paramount to account for their distinct electron configurations. Here we report an air- and moisture-stable, visible lightabsorbing Zr(iv) photosensitizer, Zr( Mes PDP Ph ) 2 , where [ Mes PDP Ph ] 2- is the doubly deprotonated form of [2,6-bis(5-(2,4,6- trimethylphenyl)-3-phenyl-1H-pyrrol-2-yl)pyridine]. This molecule has an exceptionally long-lived triplet LMCT excited state (τ = 350 μs), featuring highly efficient photoluminescence emission (Ф = 0.45) due to thermally activated delayed fluorescence emanating from the higher-lying singlet configuration with significant LMCT contributions. Zr( Mes PDP Ph ) 2 engages in numerous photoredox catalytic processes and triplet energy transfer. Our exploration offers a blueprint for future photosensitizer development featuring early transition metals and excited states with significant LMCT contributions.
The present invention relates to isolated polypeptides having beta-xylosidase activity and polynucleotides encoding the polypeptides. The invention also relates to nucleic acid constructs, vectors, and host cells comprising the polynucleotides as well as methods of producing and using the polypeptides.
Nonadiabatic Molecular Dynamics (NAMD) of excited states has been widely used in the simulation of photoinduced phenomena. However, the inability to treat bond breaking and forming processes with single-reference electronic structure methods limits their application in photochemistry for extended molecular systems. In this work, the extension of excited-state NAMD for open-shell systems is developed and implemented in the NEXMD software. We present the spin-unrestricted CIS and TDSCF formalism for the ground and excited states, analytical derivatives, and nonadiabatic derivative couplings for the respective potential energy surfaces. Next, this methodology is employed to study the photochemical reaction of three model molecules. The results demonstrate the advantage of the open-shell approach in modeling photochemical reactions, especially involving bond breaking processes. We find that the open-shell method lowers the reaction barrier at the bond-breaking limits resulting in larger calculated photochemical quantum yields compared to the respective closed-shell results. We also address problems related to spin contamination in the open-shell method, especially when molecular geometries are far from equilibrium.
It is well known that application specific computing systems, optimally designed and configured for a given workload, offer much higher energy-efficiency and throughput than general purpose systems. In modern computing systems, heterogeneous computing systems have emerged that exploit the energy and performance benefits of combining various different domain-specific processor architectures. Application domains such as high-performance computing and machine learning now process terabyte-sized data sets, requiring enormous processing and memory resources. These applications have very high-power consumption due to bottlenecks in the electrical interconnection between processing units. This project aims to reduce both communication energy and latency by integrating universally available accelerators with silicon photonics. It also aims to increase system throughput by exploiting emerging technologies in silicon photonic reconfigurable interconnects; this will allow the system to balance itself in real time to accommodate changes in workloads and data flows.
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The goal of this project is to accurately model the dynamic loads for power system planning and operation and to address the challenges inherent in such modeling. Existing load models are inadequate to handle the increasing proliferation of residential air conditioner loads and distributed energy resources, the emerging trends of price-responsive demand, and the growing importance of protective devices in equipment and buildings. A need has emerged to develop a fundamentally new class of load models and next-generation data tools. This work proposes to develop a set of regional–level, scalable open source load models and tools, including large-scale aggregate load protection, concept of price responsive demand, advanced load composition data, next-generation load model data tools, and advanced composite load model calibration and validation frameworks to address new challenges in the representation of next-generation load models.