About

The Wang lab endeavors to understand the mechanisms underlying the regulatory installation and functions of specific epitranscriptomic marks, such as pseudouridine, N6-methyladenosine and inosine. To unravel these mysteries, we are dedicated to pioneering novel assays and studying the modifications in specific biological contexts, such as during the innate immune response and neuronal development. We aim to achieve a deeper understanding of RNA modifications, and with these insights we aspire to propel the development of RNA therapeutics and the identification of novel drug targets.

Research topics

• Nanotechnology
• Materials science
• Optoelectronics
• Optics
• Chemistry
• Composite material
• Chemical engineering
• Crystallography
• Metallurgy
• Physics

Selected publications

• Inner Filter Effect-Driven Bimetallic MOFzyme for Non-Destructive Dual-Mode Alkaline Phosphatase Detection and Pathogen Identification

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Author response for "A novel approach for classifying Monoamine Neurotransmitters by applying Machine Learning on UV plasmonic-engineered Auto Fluorescence Time Decay Series (AFTDS)"

  2025-06-05

  peer-reviewSenior author
  PublisherDOI

• Author response for "A novel approach for classifying Monoamine Neurotransmitters by applying Machine Learning on UV plasmonic-engineered Auto Fluorescence Time Decay Series (AFTDS)"

  2025-09-03

  peer-reviewSenior author
  PublisherDOI

• A novel approach for classifying monoamine neurotransmitters by applying machine learning on UV plasmonic-engineered auto fluorescence time decay series (AFTDS)

  Nanoscale Advances · 2025-01-01

  articleOpen accessSenior author

  -nearest neighbors (KNN) and Random Forest (RF) demonstrate the superior performance of LSTM in distinguishing neurotransmitters. The results reveal that AlCNC substrates provide up to a 12-fold enhancement in fluorescence intensity for DA, 9-fold for NE, and 7-fold for DOPAC compared to silicon substrates. At the same time, ML algorithms achieve classification accuracy exceeding 89%. This interdisciplinary methodology bridges the gap between nanotechnology and ML, showcasing the synergistic potential of AlCNC-enhanced native fluorescence and ML in biosensing. The framework paves the way for probe-free, label-free biomolecule profiling, offering transformative implications for biomedical diagnostics and neuroscience research.

  PublisherOA PDFDOI

• Investigation of diamond/Ga₂O₃ and diamond/GaN hetero-p–n junctions using mechanical grafting

  Semiconductor Science and Technology · 2025-07-21 · 5 citations

  articleOpen accessCorresponding

  Abstract Exploring hetero-p–n junctions between ultrawide bandgap (UWBG) semiconductors is critical for advancing our understanding of carrier transport and interface properties, which are key to enabling future high-power electronic applications. However, large lattice mismatches and difficulty in doping have made such investigations particularly difficult. In this study, we introduce a unified approach for forming diamond/ β -Ga 2 O 3 and diamond/GaN hetero-p–n junctions by mechanically grafting their bulk materials, without the use of interfacial layers or complex bonding processes. The mechanically grafted diamond/ β -Ga 2 O 3 junction demonstrates a turn-on voltage of ∼3.25 V and maintains stable electrical behavior up to 125 °C, with low hysteresis (⩽0.2 V at room temperature and ⩽0.7 V at elevated temperature). A remarkably low ideality factor of 1.28 and rectification ratios exceeding 10 6 underscore the quality of the junction. The diamond/GaN heterojunction, formed on both Ga-polar and N-polar surfaces, exhibits stable diode behavior with light emission, indicating efficient charge transport. Both configurations demonstrate near-ideal characteristics, with ideality factors of 1.30 (Ga-polar) and 1.06 (N-polar), and rectification ratios exceeding 10 6 and 10 4 , respectively. The Ga-polar junction also shows notably low hysteresis (<0.05 V at 10 μ A), outperforming its N-polar counterpart. These findings highlight mechanical grafting as a practical and reproducible approach for studying heterojunctions between lattice-mismatched UWBG semiconductors. This method enables direct investigation of interface behavior and junction performance, offering value for both research and education in UWBG semiconductor technologies.

  PublisherDOI

• Inner filter effect-driven bimetallic MOFzyme for non-destructive dual-mode alkaline phosphatase detection and pathogen identification

  Sensors and Actuators B Chemical · 2025-12-05 · 1 citations

  articleSenior authorCorresponding
  PublisherDOI

• A novel approach for classifying Monoamine Neurotransmitters by applying Machine Learning on UV plasmonic-engineered Auto Fluorescence Time Decay Series (AFTDS)

  ArXiv.org · 2025-07-09

  preprintOpen accessSenior author

  This study introduces a hybrid approach integrating advanced plasmonic nanomaterials and machine learning (ML) for high-precision biomolecule detection. We leverage aluminum concave nanocubes (AlCNCs) as an innovative plasmonic substrate to enhance the native fluorescence of neurotransmitters, including dopamine (DA), norepinephrine (NE), and 3,4-Dihydroxyphenylacetic acid (DOPAC). AlCNCs amplify weak fluorescence signals, enabling probe-free, label-free detection and differentiation of these molecules with great sensitivity and specificity. To further improve classification accuracy, we employ ML algorithms, with Long Short-Term Memory (LSTM) networks playing a central role in analyzing time-dependent fluorescence data. Comparative evaluations with k-Nearest Neighbors (KNN) and Random Forest (RF) demonstrate the superior performance of LSTM in distinguishing neurotransmitters. The results reveal that AlCNC substrates provide up to a 12-fold enhancement in fluorescence intensity for DA, 9-fold for NE, and 7-fold for DOPAC compared to silicon substrates. At the same time, ML algorithms achieve classification accuracy exceeding 89%. This interdisciplinary methodology bridges the gap between nanotechnology and ML, showcasing the synergistic potential of AlCNC-enhanced native fluorescence and ML in biosensing. The framework paves the way for probe-free, label-free biomolecule profiling, offering transformative implications for biomedical diagnostics and neuroscience research.

  PublisherOA PDFDOI

• Research of A 4H-SiC MOSFET with Thick Oxide Layer and Split Gate Structure

  2024-08-08 · 1 citations

  article

  With the rapid development of SiC power semiconductor devices, SiC MOSFETs with fast switching speeds have been widely used and investigated because of their excellent electrical performance, but there are still limitations in their design and process. For example, the early failure of the gate oxide layer at the base of the trench gate, along with the issue of high power consumption during switching. These problems are also the current hotspots in the research of SiC power devices. In order to resolve these challenges, a trench-gate 4H-SiC MOSFET structure (LG-UMOS) with a thick oxide layer and a split gate design has been suggested. Its internal mechanisms and electrical properties have been examined through simulation.

  PublisherDOI

• Corrosion-enabled tryptophan biosensing enhancement on commercially available Mg alloy surfaces

  Chemical Communications · 2024-01-01 · 4 citations

  articleCorresponding

  A novel method enhances tryptophan fluorescence signals 5.45 times using a corrosion-modified magnesium alloy. Corrosion controls and stabilizes the surface morphology, resulting in a significant fluorescence enhancement. This highlights its biosensing potential with long-term stability, crucial for understanding the impact of tryptophan on metabolic and neurological disorders.

  PublisherDOI

• Rapid visual detection method of highway pavement sag based on improved Yolov7-tiny

  E3S Web of Conferences · 2024-01-01

  articleOpen access1st authorCorresponding

  Different weather and light conditions will affect the detection of pavement sag. And the camera field of view is limited, can not fully cover the entire highway road surface, resulting in higher difficulty in road sag detection. Therefore, a rapid visual detection method for highway pavement sag based on improved Yolov7-tiny is proposed. The structure elements are determined, and the small noise points in the image are removed by etching operation, and the morphology of the highway surface is completed. Input the processed highway pavement sag image into YOLOv7-tiny network, improve the input module and backbone network, and output the irregular shape of the sag image target. Fuzzy C-Means (FCM) clustering algorithm is introduced and objective function is established to realize the morphological feature visual detection of highway pavement depression. The experimental results show that: The method has smaller entropy, larger fuzzy coefficient, higher peak signal-to-noise ratio, and clearer features of pavement depression images.

  PublisherOA PDFDOI

Frequent coauthors

• Steve Blair

  University of Utah

  18 shared

• Cleumar da Silva Moreira

  Instituto Federal de Educação, Ciência e Tecnologia do Pará

  11 shared

• Berardi Sensale‐Rodriguez

  University of Utah

  10 shared

• Yuanxiang Zhou

  8 shared

• Xueling Cheng

  University of Utah

  8 shared

• Jieying Mao

  6 shared

• Ninghua Wang

  Peking University

  6 shared

• Rossana Moreno Santa Cruz

  Instituto Federal de Educação Ciência e Tecnologia da Paraíba

  5 shared

Labs

• Wang LabPI