About

Dr. Z. Hong Zhou is a Professor in the Microbiology, Immunology, and Molecular Genetics department at UCLA. He combines expertise in structural biology, microbiology, computational biology, and bioinformatics to address fundamental questions in biology. He is also the founding director of the Electron Imaging Center for NanoMachines (EICN) at the California Nanosystems Institute (CNSI). Dr. Zhou's research focuses on using cryogenic electron microscopy (cryoEM) and cryogenic electron tomography (cryoET) to visualize biological complexes, which helps in understanding their function and organization. His work involves developing these methods and applying them to study dynamic processes such as viral and microbial pathogenesis. Dr. Zhou has received numerous awards, including being a Pew Scholar in the Biomedical Sciences, the Basil O’Connor Scholar Award, the Established Investigator Award of the American Heart Association, the Burton award, and the KH Kuo Award of Distinguished Scientist. In 2024, he was elected a Fellow of the American Academy of Microbiology.

Research topics

• Biology
• Genetics
• Computational biology
• Chemistry
• Cell biology
• Virology

Selected publications

• Ultra-wide-field, deep, adaptive two-photon microscopy for multi-scale neuronal imaging

  Light Science & Applications · 2026-04-13 · 1 citations

  articleOpen access

  Abstract Observing the activity patterns of large neural populations throughout the brain is essential for understanding brain function. However, capturing neural interactions across widely distributed brain regions from both superficial and deep cortical layers remains challenging with existing microscopy technologies. Here, we introduce a state-of-the-art two-photon microscopy system, ULTRA, capable of single-cell resolution imaging across an ultra-large field of view (FOV) exceeding 50 mm², enabling deep and wide field in vivo imaging. To demonstrate its capabilities, we conducted a series of experiments under multiple imaging conditions, successfully visualizing brain structures and neuronal activities spanning a spatial range of over 7 mm from superficial layers to depths of up to 900 μm, while covering a volume of 45.24 mm 3 in the mouse brain. This versatile imaging platform overcomes traditional spatial constraints, providing a powerful tool for comprehensive exploration of neuronal circuitry over extensive spatial scales with cellular resolution.

  PublisherOA PDFDOI

• Laboratory investigation on hydrothermal response of the highway embankments to variation rainfall in permafrost regions

  Cold Regions Science and Technology · 2026-03-30

  article
  PublisherDOI

• Highly Aligned Bacteria Cellulose Yarn Aggregation for Energy Generation and Strain Sensing

  Advanced Science · 2026-03-19

  articleOpen accessCorresponding

  Invoking high-performance bio-based fibres (e.g., Bacterial cellulose) contributes to the sustainability and functionality of wearable electronic devices at the material level. However, the fabrication of self-powered and high-mechanosensitive stretchable BC-based sensors is challenging due to the difficulty in adaptable soft-rigid triboelectrical interfaces and obtaining ordered conductive bacterial cellulose fibres. Here, inspired by the spiral construction from biological systems, we develop an innovative bio-fabrication strategy to develop a core-sheath yarn that features the ordered network and mechanosensitive twisting structures. The yarn sensor integrates the complementary advantages of triboelectric and resistive responses for the integration of strain sensing and energy self-sufficiency. Converging factors of core-sheath structure, modulus-mismatch-governed elongation, and network cracks give the yarn sensor a sensitive mechanosensitive response (8.246), a wide strain range (up to 100%), and high voltage signals (over 50 V). The scalable self-powered fabrics based on yarns are also used as wearable power generation and energy storage for charging the yarn sensing system, achieving continuous health monitoring. The design of the unique structure assists the BC-based sensors to effectively energy charging and driving healthy monitoring system. These empirical insights from bio-manufacturing techniques to structural design of ordered yarns pave the way to obtaining multi-functional high-performance bio-based sensors.

  PublisherDOI

• Forest Succession Shapes Soil microbial Communities through Region-specific Edaphic Filters in Tropical and Subtropical Forests

  Microbial Ecology · 2026-03-23

  articleOpen access

  Understanding how soil microbial communities respond to forest succession is essential for predicting ecosystem functions and biogeochemical stability. We investigated bacterial and fungal communities across three successional stages (early, mid, late) and three soil depths (0–10, 10–20, 20–50 cm) in forests of Pakistan and China using high-throughput amplicon sequencing of 108 soil samples. This cross-regional, depth-resolved study aimed to determine whether microbial successional trajectories and soil–microbe relationships are general or region-specific. Preliminary results showed that the forest succession was accompanied higher soil organic carbon (SOC), total nitrogen (TN) and declined soil pH in Pakistan. It indicates consistent acidification and potential phosphorus limitation in mature stands. Whereas SOC and total potassium (TK) exhibited mid-successional peaks in China, that indicates different resource/nutrient dynamics. Mid-forest successional stages showed maximum bacterial diversity, whereas late succession revealed the highest fungal diversity (kingdom-specific responses). Community composition shifted from copiotrophic taxa in early stages to oligotrophic taxa in mature forests. Soil pH was the most influential factor shaping microbial composition in Pakistan, whereas potassium availability was the most influential factor in China. These cross-regional, depth-resolved results reveal both successional patterns and region-specific environmental controls, offering new insights into microbial community composition during forest development and providing guidance for forest restoration and soil-carbon management across biogeographically diverse regions.

  PublisherDOI

• Pancake‐Like Surface Halide‐Fluorothiolate‐Enriched Au ₁₂ Ag ₃₀ Nanoclusters for Defect Passivation and Moisture Shielding in Perovskite Solar Cells

  Angewandte Chemie · 2026-03-20

  article

  ABSTRACT There are few examples of the function‐oriented synthesis of atomically precise metal nanoclusters (NCs) whose ligand composition and geometry readily translate into device‐level performance. Here, we report the design and synthesis of the pancake‐like [Au 12 Ag 30 (SPh 3,5− (CF 3 ) 2 ) 10 Br 20 ] 6− (Au 12 Ag 30 ) NC, and its use to boost the power conversion efficiency (PCE) and durability of perovskite solar cells. Single‐crystal x‐ray diffraction of Au 12 Ag 30 revealed an Au 12 icosahedron wrapped by a six‐layer Ag 30 shell, the (100) facets of which are capped by 20 Br − ligands, with 10 ‐CF 3 ‐bearing thiolates forming a hydrophobic equatorial belt. Introducing 0.5 mg/mL of this cluster into ((FA 0.95 Cs 0.05 )PbI 3 ) 0.975 (MAPbBr 3 ) 0.025 perovskite films boosted the device PCE from 23.55% to 25.53% and enables 94.8% retention after 1142 h of continuous one‐sun illumination. Au 12 Ag 30 is also distinguished by its −6 superatomic charge, and 66.7% surface halogen coverage—the highest ever reported. These findings establish anisotropic halide/thiolate‐protected NCs as potent additives for simultaneous defect passivation and environmental protection in high‐efficiency optoelectronics.

  PublisherDOI

• Pancake‐Like Surface Halide‐Fluorothiolate‐Enriched Au ₁₂ Ag ₃₀ Nanoclusters for Defect Passivation and Moisture Shielding in Perovskite Solar Cells

  Angewandte Chemie International Edition · 2026-03-20

  articleCorresponding

  ABSTRACT There are few examples of the function‐oriented synthesis of atomically precise metal nanoclusters (NCs) whose ligand composition and geometry readily translate into device‐level performance. Here, we report the design and synthesis of the pancake‐like [Au 12 Ag 30 (SPh 3,5− (CF 3 ) 2 ) 10 Br 20 ] 6− (Au 12 Ag 30 ) NC, and its use to boost the power conversion efficiency (PCE) and durability of perovskite solar cells. Single‐crystal x‐ray diffraction of Au 12 Ag 30 revealed an Au 12 icosahedron wrapped by a six‐layer Ag 30 shell, the (100) facets of which are capped by 20 Br − ligands, with 10 ‐CF 3 ‐bearing thiolates forming a hydrophobic equatorial belt. Introducing 0.5 mg/mL of this cluster into ((FA 0.95 Cs 0.05 )PbI 3 ) 0.975 (MAPbBr 3 ) 0.025 perovskite films boosted the device PCE from 23.55% to 25.53% and enables 94.8% retention after 1142 h of continuous one‐sun illumination. Au 12 Ag 30 is also distinguished by its −6 superatomic charge, and 66.7% surface halogen coverage—the highest ever reported. These findings establish anisotropic halide/thiolate‐protected NCs as potent additives for simultaneous defect passivation and environmental protection in high‐efficiency optoelectronics.

  PublisherDOI

• The Impact of Artificial Intelligence-Enabled Virtual Simulation on Clinical Reasoning in Dental Students in Managing Temporomandibular Disorders

  Research Square · 2026-03-10

  preprintOpen access
  PublisherOA PDFDOI

• EGFR-Net: Hierarchical Edge-Guided Representation Learning for Underwater Object Detection

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Dual‐Stabilized Single‐Site Fe(II) for Catalytic Conversion of CO ₂ to Propiolic Acid Under Ambient Condition

  EcoEnergy · 2026-04-01

  articleOpen access

  ABSTRACT Efficient conversion of carbon dioxide to organics has been a promising outlet for value‐added carbon dioxide consumption. However, most of the catalysts developed for this process currently focus on expensive noble metal catalysts, which severely limits their further evolution and application. Herein, a precisely designed dual‐stabilized single‐site Fe(II) catalytic system is developed through combination of the covalent interaction between the β‐diketiminate ligand and the hyperactive Fe(II) center and the host–guest interaction between the porous matrix and active species, which realized the most efficient production of propiolic acid from CO 2 and terminal alkynes under ambient conditions with stable multiple recycling. The advantages of both homogeneous and heterogeneous catalysts are achieved in a single‐site Fe(II) catalytic system, whose performance surpasses the known noble metal catalysts in terms of yield. Density functional theory model calculations are employed to explore the optimal pathways and mechanisms for the catalytic conversion of CO 2 to propiolic acid over the prepared catalytic platform.

  PublisherDOI

• Research on Metro Transportation Flow Prediction Based on the STL-GRU Combined Model

  2026-01-28

  article

  In the metro intelligent transportation system, accurate transfer passenger flow prediction is a key link in optimizing operation plans and improving transportation efficiency. To further improve the theory of metro internal transfer passenger flow prediction and provide more reliable support for intelligent operation decisions, this paper innovatively proposes a metro transfer passenger flow prediction model that integrates the Seasonal and Trend decomposition using Loess (STL) method and Gated Recurrent Unit (GRU).In practical application, the model first relies on the deep learning library Keras to complete the construction and training of the GRU model, laying the foundation for subsequent prediction; then preprocesses the original metro card swiping data, uses the graph-based depth-first search algorithm to identify passengers' travel paths, and further constructs the transfer passenger flow time series; subsequently adopts the STL time series decomposition algorithm to decompose the constructed transfer passenger flow time series into trend component, periodic component and residual component, and uses the 3σ principle to eliminate and fill the outliers in the residual component, and finally completes the transfer passenger flow prediction. Taking the transfer passenger flow data of a certain metro station as the research sample, the validity of the model is verified. The results show that compared with Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and the combined model of STL time series decomposition method and Long Short-Term Memory (STL-LSTM), the STL-GRU combined prediction model significantly improves the prediction accuracy of transfer passenger flow on weekdays (excluding Fridays), Fridays and rest days, with the mean absolute percentage error (MAPE) of the prediction results reduced by at least 2.3, 1.36 and 6.42 percentage points respectively.

  PublisherDOI

Recent grants

• Molecular analyses of toxin nanopore structural dynamics

  NIH · $437k · 2016–2019

• NIH Grant R01CA094809

  NIH · $1.3M · 2008

• In situ atomic structures of the Kaposi's sarcoma-associated herpesvirus portal-terminase complex and glycoproteins

  NIH · $4.0M · 2015–2025

• Composition, structure, and mutational analysis of the Francisella Type VI Secretion System

  NIH · $625k · 2018–2020

• NIH Grant S10RR023057

  NIH · $3.2M · 2007

Frequent coauthors

• Yun-Tao Liu

  University of California, Los Angeles

  93 shared

• Guo‐Qiang Bi

  University of Science and Technology of China

  84 shared

• P. Ge

  Wuhan University

  76 shared

• Shiheng Liu

  Shanghai University

  75 shared

• Jiansen Jiang

  National Institutes of Health

  75 shared

• Yanxiang Cui

  Jimo District Qingdao Hospital of Traditional Chinese Medicine

  75 shared

• Hui Wang

  University of California, Los Angeles

  74 shared

• Xinghong Dai

  Hunan University

  53 shared

Awards & honors

• Pew Scholar, The Pew Charitable Trust (1999)
• Basil O’Connor Scholar Award
• Established Investigator Award of American Heart Association
• Burton award
• KH Kuo Award of Distinguished Scientist