About

Zhihao Zhang is an Assistant Professor of Business Administration in the Marketing area at the UVA Darden School of Business. He teaches the marketing core course for the full-time MBA program. His research draws from academic training and research experience in consumer behavior and cognitive neuroscience, focusing on understanding the cognitive, computational, and neuroscientific mechanisms by which memory and knowledge—such as those related to brands, products, services, or social interactions—shape decision-making. He has a particular interest in using neuroscience to inform real-world problems at the intersection of marketing and law, including issues like trademark and copyright infringement. His work has been published in leading academic journals such as Proceedings of the National Academy of Sciences, Science Advances, Nature Communications, and Current Biology, and has been covered by major media outlets including BBC News, Los Angeles Times, Fortune, and others. Before joining Darden, he was a postdoctoral scholar at the Haas School of Business, University of California, Berkeley. He holds an undergraduate degree from Tsinghua University and a Ph.D. from Yale University’s Interdepartmental Neuroscience Program.

Research topics

• Materials science
• Chemistry
• Nanotechnology
• Chemical engineering
• Chemical physics
• Electrical engineering
• Physical chemistry
• Metallurgy
• Optoelectronics

Selected publications

• Solar-driven water splitting with ascorbic acid oxidation for efficient hydrogen production

  Energy & Environmental Science · 2025-01-01 · 7 citations

  article

  By integrating the AAOR with the HER in a solar-powered membrane-free system, this research achieves a 3.5-fold reduction in energy consumption compared to conventional electrolysis while generating additional economic value from DHA production.

  PublisherDOI

• HS–SPME–GC–MS combined with machine learning methods for screening volatile quality indicators in <i>Hypericum perforatum</i> L.

  New Journal of Chemistry · 2025-01-01

  article1st authorCorresponding

  Hypericum perforatum L. (HPL), a natural product with high medicinal value, exhibits diverse bioactivities.

  PublisherDOI

• Author response for "HS-SPME-GC-MS combined with machine learning methods for screening volatile quality indicators in Hypericum perforatum L"

  2025-04-26

  peer-review1st authorCorresponding
  PublisherDOI

• A Machine Learning‐Based Approach for the Prediction of Anticoagulant Activity of <scp><i>Hypericum perforatum</i></scp> L. and Evaluation of Compound Activity

  Phytochemical Analysis · 2024-11-17 · 1 citations

  articleOpen access1st author

  INTRODUCTION: Hypericum perforatum L. (HPL) is extensively researched domestically and internationally as a medicinal plant. However, no reports of studies related to the anticoagulant activity of HPL have been retrieved. The specific bioactive components are unknown. OBJECTIVE: The aim of this study was to develop a machine learning (ML) method for rapid prediction of anticoagulant activity in HPL and evaluation of compound activity. MATERIALS AND METHODS: First, an in vitro anticoagulant activity assay was developed for the determination of the bioactivity of various medicinal parts of HPL. Then, the peak areas of compounds in HPL were integrated using UPLC-Q-TOF-MS analysis. Subsequently, nine independent ML methods and two ensemble learning methods have been established to predict the anticoagulant activity of HPL and to evaluate the contribution of compounds. Feature importance scores were used for models visualization. RESULTS: A total of 24 compounds were shown to exhibited superior anticoagulant activity. Molecular docking experiments likewise confirmed this result. The results show that the branches of HPL have excellent anticoagulant activity, which has been previously overlooked. The established ML model demonstrated good performance in the prediction of the activity of HPL. CONCLUSION: The results were accurate and reliable, which significantly improved the efficiency of active compounds screening, and further exploration in this area is warranted.

  PublisherOA PDFDOI

• Unravelling heterogenous adsorption performance of hydrochar particle and key properties in heavy metal immobilization relative to corresponding residual bulk hydrochar

  Process Safety and Environmental Protection · 2024-10-21 · 5 citations

  articleCorresponding
  PublisherDOI

• Efficient BiVO₄ Photoanode with an Excellent Hole Transport Layer of CuSCN for Solar Water Oxidation

  Advanced Energy Materials · 2024-02-27 · 57 citations

  articleOpen access

  Abstract Bismuth vanadate (BiVO 4 ) is reported as a key material in photoelectrocatalysis owing to high theoretical efficiency, relatively narrow band gap of 2.4 eV, and favorable conduction band edge position for hydrogen evolution. However, the sluggish hole transport dynamics lead to slow photogenerated charge separation and transport efficiencies, which result in charge recombination due to aggregation. Herein, a novel hole transport layer of copper(I) thiocyanate (CuSCN) with the aim of significantly enhancing the efficiency of charge transport and stability of BiVO 4 photoanodes is reported. The introduction of the hole transport layer could provide an appropriate intermediate energy level for photogenerated hole transfer and avoid charge recombination and trapping. After a photoassisted electrodeposition process of NiCoFe‐B i catalysis, the obtained photoanode achieves a photocurrent density of 5.6 mA cm −2 at 1.23 V versus reversible hydrogen electrode under AM 1.5 G simulated solar radiation, and an applied bias photon to current efficiency of 2.31%. With the CuSCN layer, BiVO 4 photoanode presented impressive stable photocurrent during 50 h continuous illumination. Meanwhile, the unbiased tandem device of the NiCoFe‐B i /CuSCN/BiVO 4 photoanode and the Si solar cell exhibit a solar‐to‐hydrogen efficiency of 5.75% and excellent stability for 14 h.

  PublisherOA PDFDOI

• Population Pharmacokinetics and Dosing Optimization of Norvancomycin for Chinese Patients with Community-Acquired Pneumonia

  Infection and Drug Resistance · 2024-12-01

  articleOpen access

  Purpose: Determining the optimal dosage of norvancomycin (NVCM) for Chinese patients with community-acquired pneumonia (CAP) caused by gram-positive cocci remains uncertain. This research aimed to identify influential factors affecting NVCM pharmacokinetics and explore optimal dosage regimens via population pharmacokinetic (PPK) analysis. Patients and Methods: A prospective analysis was conducted at the Second Hospital of Hebei Medical University (Shijiazhuang, China). CAP patients aged ≥ 18 years and receiving intravenous NVCM were enrolled. Each patient underwent the collection of 3– 8 blood samples for analysis during the treatment. Nonlinear mixed effect model (NONMEM) software was used to develop PPK models, while Monte Carlo simulations were employed to optimize dose regimens. Pharmacokinetic-pharmacodynamic (PK/PD) breakpoint was defined as daily area under the concentration on the second day of therapy to minimum inhibitory concentration ratio (AUC 24-48h /MIC) ≥ 361, and a steady-state AUC to MIC radio (AUC ss,24h /MIC) ≥ 361. Results: A prospective PPK analysis of 231 NVCM concentrations was performed in 34 patients. A two-compartment model with first-order elimination adequately described the pharmacokinetics. The population typical clearance (CL) of NVCM was 3.15 L/h, and the central volume of distribution was 12.3 L. Notably, CL exhibited significant correlations with age and serum creatinine (Scr) levels. For mild or moderate CAP patients, the recommended doses were 400– 800 mg every 12 h to achieve the target exposure with AUC ss,24h /MIC ≥ 361. For community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA) pneumonia, the suggested dosage regimen was 600– 800 mg every 8 h, which could achieve the target exposure preferably within the initial 24 to 48 h. Conclusion: Age and Scr levels significantly influenced the pharmacokinetic parameters of NVCM in CAP patients. Our model-informed precision dosing approach may help for early optimization of NVCM exposure. Further prospective studies with larger samples will be needed. Keywords: norvancomycin, population pharmacokinetics, community-acquired pneumonia, dosing optimization

  PublisherOA PDFDOI

• Photoelectrochemical water oxidation through a hybrid architecture nanoparticles/nanotubes

  Applied Surface Science · 2023-06-10 · 4 citations

  article
  PublisherDOI

• HierSyn: Fast Synthesis for Large Hierarchical Designs

  2023-10-24 · 2 citations

  article

  As design goes into multi-billion transistors, the synthesis runtime becomes an important issue, particularly for design verification and prototyping, as one may run the synthesis many times with design change. Module-by-module synthesis with multi-threading is a natural solution for fast synthesis, however, at the cost of quality of results (QoR) degradation. Besides, multi-thread speedup may not be so good due to very uneven sizes of the modules. In this paper, we propose a design hierarchy restructuring based multi-thread synthesis algorithm for large-scale designs. Small module flattening and large module partitioning are used to create moderate size design modules. Our experimental results show that our algorithm can produce results within 3% area increase and 21.3x speedup over the flat synthesis flow.

  PublisherDOI

• Modifying engineered nanomaterials to produce next generation agents for environmental remediation

  The Science of The Total Environment · 2023-06-19 · 50 citations

  review
  PublisherDOI

Frequent coauthors

• Sen Zhang

  27 shared

• Wenzhen Li

  Iowa State University

  16 shared

• Sheng Dai

  Oak Ridge National Laboratory

  15 shared

• Le Xin

  Zibo Vocational Institute

  12 shared

• Chang Liu

  University of Science and Technology of China

  10 shared

• John T. Brosnahan

  University of Virginia

  9 shared

• Hua Zhou

  Argonne National Laboratory

  9 shared

• Meiyang Cui

  University of Virginia

  8 shared