Engineering topics
Wen, Bo
Publications and source records attributed to Wen, Bo.
Frequency Response Analysis to Monitor and Identify Changes in the Impedance of a Photovoltaic Panel Measured Online using a Power Optimizer
Photovoltaic (PV) cells are generally modeled as a current source due to photocurrent, p-n junction diodes with parasitic resistance, capacitance, and inductance. This paper proposes online frequency response analysis (FRA) to measure the impedance of a PV panel using an existing panel-level power optimizer in a PV system. The algorithm will actively perturb a small signal into a 300 W rooftop PV panel and compute its small signal impedance. The technology discussed is easy to incorporate, requires no additional hardware, doesn't alter the stability of the system, and is implemented at a steady-state point. The power optimizer initially stabilizes at an operating point and then perturbs the PV current via FRA and computes PV panel impedance. The relative standard deviation test conducted indoors under 300 W/m 2 illumination on a PV panel shows a less than 5% error rate in PV panel impedance magnitude and phase is measured using a power optimizer.
Proteogenomic insights suggest druggable pathways in endometrial carcinoma
We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity. PIK3R1 in-frame indels are associated with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors. CTNNB1 hotspot mutations are concentrated near phosphorylation sites mediating pS45-induced degradation of β-catenin, which may render Wnt-FZD antagonists ineffective. Deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for more precise treatment of EC.
High-Efficiency Modular SiC-based Power-Converter for Flexible-CHP Systems with Stability-Enhanced Grid-Support Functions
This project seeks to develop a modular, scalable MV power converter featuring stability-enhanced grid-support functions for future grid-interface applications in flexible combined heat and power (F-CHP) cogeneration plants, being fully compliant with the IEEE Standard 1547, category B—for operation in local areas with high aggregated distributed energy resource (DER) penetration, and also with the IEEE standard for the specification of microgrid controllers, namely IEEE Std 2030.7, with the goal to enable F-CHP systems for both microgrid and standalone applications. Further, the proposed converter will use a modular circuit topology, the MMC, which is scalable both in voltage and current by interconnecting power-cell building blocks, thus flexibly suiting the needs of F-CHP systems in the 1–20 MWe range. Furthermore, the use of 10 kV SiC MOSFET devices will minimize the number of power-cells needed to operate in 2–13.8 kV MV distribution systems, but more importantly, they will render feasible a power conversion efficiency > 98 %, and a power density > 10 kW/l. This is highly relevant given that these are two key performance metrics that will further increase the value of F-CHP systems by shortening the time required to recover their investment costs.
Discovery of a Potent and Selective STAT5 PROTAC Degrader with Strong Antitumor Activity In Vivo in Acute Myeloid Leukemia
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Water dissociation at the water–rutile TiO 2 (110) interface from ab initio-based deep neural network simulations
The interaction of water with TiO 2 surfaces is of crucial importance in various scientific fields and applications, from photocatalysis for hydrogen production and the photooxidation of organic pollutants to self-cleaning surfaces and bio-medical devices. In particular, the equilibrium fraction of water dissociation at the TiO 2 –water interface has a critical role in the surface chemistry of TiO 2 , but is difficult to determine both experimentally and computationally. Among TiO 2 surfaces, rutile TiO 2 (110) is of special interest as the most abundant surface of TiO 2 ’s stable rutile phase. While surface-science studies have provided detailed information on the interaction of rutile TiO 2 (110) with gas-phase water, much less is known about the TiO 2 (110)–water interface, which is more relevant to many applications. In this work, we characterize the structure of the aqueous TiO 2 (110) interface using nanosecond timescale molecular dynamics simulations with ab initio-based deep neural network potentials that accurately describe water/TiO 2 (110) interactions over a wide range of water coverages. Simulations on TiO 2 (110) slab models of increasing thickness provide insight into the dynamic equilibrium between molecular and dissociated adsorbed water at the interface and allow us to obtain a reliable estimate of the equilibrium fraction of water dissociation. We find a dissociation fraction of 22 ± 6% with an associated average hydroxyl lifetime of 7.6 ± 1.8 ns. These quantities are both much larger than corresponding estimates for the aqueous anatase TiO 2 (101) interface, consistent with the higher water photooxidation activity that is observed for rutile relative to anatase.
Power Electronics Based Self-Monitoring and Diagnosing for Photovoltaic Systems
Faults in photovoltaic (PV) systems can seriously affect the efficiency, energy yield, cost, safety, and reliability of PV plants. Condition monitoring of PV plants is, therefore, a very important approach to estimating the health condition of PV modules and power electronics in the system. However, additional hardware for PV system monitoring adds cost to the system's operation; delayed maintenance service also causes additional energy production loss. The Center for Power Electronics Systems (CPES) at the Virginia Polytechnic Institute and State University and Siemens Cooperate Research developed the online impedance measurement for a PV panel self-monitoring and diagnosing technology using the DC-DC converter connected to the panel. Small-signal impedances of a monocrystalline silicon PV panel were modeled and simulated to reflect fault conditions such as the short-circuit, hot-spot, and junction box faults. Modeling and simulation results were validated firstly using a test setup consisting of a solar simulator, a network analyzer, small-signal injectors, and a monocrystalline PV panel rated at 300 W.
Power Electronics Based Self-Monitoring and Diagnosing for Photovoltaics Systems
Self-monitoring and diagnosing technology for photovoltaic (PV) systems is a method to reduce energy production losses. The proposed technology will enable existing panel-level power optimizers and inverters in a PV system to actively perturb the system, measure its response to these small-signal perturbations, and detect any changes in the small-signal impedances. Impedance measurement will be used to identify specific faults and power degradation trends in a PV panel. This information can be used to instantly alert Operations and Maintenance (O&M) personnel of the need for corrective action, thereby reducing energy production losses earlier relative to standard PV systems.
Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification
Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson’s correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.
Discovery of EEDi-5273 as an Exceptionally Potent and Orally Efficacious EED Inhibitor Capable of Ac
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Increasing Iridium Oxide Activity for the Oxygen Evolution Reaction with Hafnium Modification
Synthesis and implementation of highly active, stable, and affordable electrocatalysts for the oxygen evolution reaction (OER) is a major challenge in developing energy efficient and economically viable energy conversion devices such as electrolyzers, rechargeable metal-air batteries, and regenerative fuel cells. The current benchmark electrocatalyst for OER is based on iridium oxide (IrO x ) due to its superior performance and excellent stability. However, large scale applications using IrO x are impractical due to its low abundance and high cost. In this work, we report a highly active hafnium-modified iridium oxide (IrHf x O y ) electrocatalyst for OER. The IrHf x O y electrocatalyst demonstrated ten times higher activity in alkaline conditions (pH = 11) and four times higher activity in acid conditions (pH = 1) than a IrO x electrocatalyst. The highest intrinsic mass activity of the IrHf x O y catalyst in acid conditions was calculated as 6950 A gIrO x -1 at an overpotential (η) of 0.3 V. Combined studies utilizing operando surface enhanced Raman spectroscopy (SERS) and DFT calculations revealed that the active sites for OER are the Ir-O species for both IrO x and IrHf x O y catalysts. The presence of Hf sites leads to more negative charge states on nearby O sites, and shortening the bond lengths of Ir-O, and lowering free energies for OER intermediates to accelerate the OER process.
A proteogenomic portrait of lung squamous cell carcinoma
Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.
Hydrogen Bonds and H 3 O + Formation at the Water Interface with Formic Acid Covered Anatase TiO 2
Carboxylic acid-modified TiO 2 surfaces in aqueous environment are of widespread interest, yet atomic-scale understanding of their structure is limited. In this work, we investigate formic acid (FA) on anatase TiO 2 (101) (A-101) in contact with water using density functional theory (DFT) and ab-initio molecular dynamics (AIMD). Isolated FA molecules adsorbed in a deprotonated bridging bidentate (BD) form on A-101 are found to remain stable at the interface with water, with the acid proton transferred to a surface oxygen to form a surface bridging hydroxyl (O br H). With increasing FA coverage, adsorbed monolayers of only BD and successively of alternating monodentate (MD) and BD species give rise to a higher concentration of surface O br H’s. Simulations of these adsorbed monolayers in water environment show that some protons are released from the surface O br H’s to water resulting in a negatively charged surface with nearby solvated H 3 O + ions. These results provide insight into the complex acid-base equilibrium between an oxide surface, adsorbates and water and can also help obtain a better understanding of the wetting properties of chemically modified TiO 2 surfaces.
Structure and Stability of Pristine and Carboxylate-Covered Anatase TiO 2 (001) in Aqueous Environment
The interactions of carboxylic acids and water with TiO 2 surfaces are important in applications ranging from solar cells to biomedical devices. Here we focus on the aqueous interface with the minority (001) surface of anatase TiO 2 (A-001) and the behavior of adsorbed formic and acetic acid monolayers at this interface. We investigated the structure and stability of the pristine reconstructed and formic/acetic acid covered A-001 surfaces in contact with water using density functional theory (DFT) calculations and ab initio molecular dynamics (AIMD) simulations. The (1 × 4) reconstruction of the pristine surface is found to be stable in aqueous environment, within the time scale of our simulation. Here, carboxylic acids adsorb in deprotonated bidentate (BD) form on A-001, with the dissociated proton transferred to a surface oxygen to form a bridging hydroxyl. Of the two possible configurations, BD bridging and BD straddling, of the adsorbed species, the latter is found to rapidly transform to a monodentate structure during our simulations. Further investigation of mixed acetate–formate monolayers on A-001 in water indicates that also BD bridging species can become unstable at the boundaries between formate and acetate-covered regions, transforming to a monodentate form that does not prevent water adsorption on the TiO 2 surface.
Discovery of first-in-class inhibitors of ASH1L histone methyltransferase with anti-leukemic activity
ASH1L histone methyltransferase plays a crucial role in the pathogenesis of different diseases, including acute leukemia. While ASH1L represents an attractive drug target, developing ASH1L inhibitors is challenging, as the catalytic SET domain adapts an inactive conformation with autoinhibitory loop blocking the access to the active site. Here, by applying fragment-based screening followed by medicinal chemistry and a structure-based design, we developed first-in-class small molecule inhibitors of the ASH1L SET domain. The crystal structures of ASH1L-inhibitor complexes reveal compound binding to the autoinhibitory loop region in the SET domain. When tested in MLL leukemia models, our lead compound, AS-99, blocks cell proliferation, induces apoptosis and differentiation, downregulates MLL fusion target genes, and reduces the leukemia burden in vivo. This work validates the ASH1L SET domain as a druggable target and provides a chemical probe to further study the biological functions of ASH1L as well as to develop therapeutic agents.
Structure and Reactivity of Pristine and Reduced Spinel CoFe 2 O 4 (001)/(100) Surfaces
Cobalt ferrite, CoFe 2 O 4 (CFO), nanocrystals are efficient and competitive anode materials in the field of electrochemical water splitting. Using density functional theory with on-site Hubbard U corrections (DFT+U), we have investigated the structural, electronic and magnetic properties of CFO (001)/(100) surfaces, as well as their reactivities towards water adsorption. Special attention has been focused on the formation of oxygen vacancies (V O ), due to their key role in the oxidation activity of metal oxides, often based on the Mars-van-Krevelen mechanism. Our results show that vacancy formation is easiest at oxygen sites that are not bound to tetrahedrally-coordinated Fe. Water adsorbs mainly in molecular form on the Co/Fe metal cations, whereas it dissociates at defects. In comparison to other spinels, CFO is similar to NiFe 2 O 4 , exhibiting relatively low energy cost of V O formation and a strong affinity of the vacancies towards water. Furthermore, these findings suggest that CFO may be a more promising oxidation catalyst than NiCo 2 O 4 and Co 3 O 4 .
Conversion of Formic Acid on Single- and Nano-Crystalline Anatase TiO 2 (101)
Understanding thermochemical transformations of formic acid (FA) on metal oxide surfaces is important for many catalytical reactions. Here we study thermally induced reactions of FA on a single-crystalline and nanocrystalline anatase TiO 2 (101). We employ a combination of scanning tunneling microscopy (STM), temperature-programmed desorption (TPD), infrared reflection absorption spectroscopy (IRAS), diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), and density functional theory (DFT) to follow the FA surface intermediates and reaction products above room temperature. We find that the primary reaction products desorbing at about 300, 480, and 515 K are molecular water, carbon monoxide, and formaldehyde, respectively. Bidentate (BD) formate and bridging hydroxyl (HO b ) are identified as central intermediates in the FA transformations. Bridging oxygen vacancies (V O ) are also likely participants despite their low stability at the surface. In conclusion, the parallel studies on single crystals and faceted TiO 2 (101) nanoparticles reveal the spectroscopic commonalities of surface species and of the thermal conversion of molecular and deprotonated forms of FA.
Polaron-Adsorbate Coupling at the TiO 2 (110)-Carboxylate Interface
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