About

Dr. Qin Wang is a Professor at the College of Agriculture & Natural Resources at the University of Maryland. Her expertise encompasses food polymers, biophysics, food nanotechnology, and food chemistry. Her research focuses on the functionality evaluation and development of delivery systems derived from nanoparticles and films made from food proteins and polysaccharides, such as zein, soy protein isolate, whey protein, wheat gluten, chitosan, and its derivatives. She is involved in biosensor development through electrodeposition and quorum sensing of magnetic nanofillers and biopolymer films, as well as antimicrobial packaging material development utilizing food proteins and silver. Additionally, her work includes protein modification to enhance functional properties and nanostructure formation, alongside safety, quality, and sensory evaluations of microgreens in comparison with mature counterparts and sprouts. Dr. Wang's research group is dedicated to the design, characterization, and evaluation of natural polymers, spanning multidisciplinary areas including food science, materials science, nanotechnology, and biophysics. Her major research directions include the development of molecular-level, nano-scale vesicles and structures for encapsulating drugs and bioactive compounds for applications in delivery systems and regenerative medicine. She also advances nanotechnology approaches to better understand the physicochemical properties and interactions of food components, and the development of nano-sensors for food safety evaluation. Her educational background includes a Ph.D. in Food Science/Food Engineering from the University of Illinois at Urbana-Champaign, an M.S. in Environmental Chemistry, and a B.S. in Environmental Chemistry from Nanjing University.

Research topics

• Materials science
• Computer Science
• Microbiology
• Optoelectronics
• Biochemical engineering
• Chemistry
• Telecommunications
• Internal medicine
• Composite material
• Medicine
• Biology
• Biochemistry
• Engineering
• Nanotechnology
• Immunology

Selected publications

• Global optimization of junction-to-case thermal resistance in TO-247 power MOSFETs using hybrid response surface methodology

  Case Studies in Thermal Engineering · 2025-11-13

  articleOpen access

  Power MOSFETs face thermal reliability challenges due to multi-parametric coupling in packaging design. This work proposes a hybrid response surface methodology (RSM) framework that integrates discrete material screening (epoxy/solder/frame) and quadratic continuous-variable modeling (chip area/thickness, and solder/frame thickness). A five-layer equivalent chip model was validated with an experimental-simulation alignment of 98.09%. Furthermore, the RSM model achieved a prediction accuracy of 93.97%, enabling the global optimization of junction-to-case thermal resistance (R jc ). Optimized parameters reduced R jc by 32.97% (to 0.343 °C/W) compared to the baseline, resulting in a 10.84% decrease in junction temperature across the 10–160 W load range. Compared to orthogonal experimental design, RSM yielded 12.45% lower R jc at higher optimization efficiency. Thermal-mechanical analysis confirmed structural integrity with stress within safety limits. This approach provides an efficient pathway for multi-parametric thermal management in power electronics.

  PublisherDOI

• Synthesis of polyoxalate diols from dimethyl oxalate through transesterification-polycondensation reaction and their application in preparing novel polyurethane elastomers

  Polymer · 2025-11-11

  articleOpen access

  Growing environmental awareness greatly promotes the development of transforming coal resources into high-value added chemicals in a diversified, low-carbon and cleaner way. Dimethyl oxalate (DMO) is one of the modern coal chemical products, and it is of great significance to develop its new downstream products. In this work, polyoxalate diols (PODL) were facilely synthesized from DMO and small molecule diols via transesterification and polycondensation reactions. Further, a series of polyurethanes (PU) with excellent mechanical properties, heat resistance (T d > 300°C) and solvent resistance were prepared from polyoxalate diols and 4, 4′-diphenylmethane diisocyanate (MDI). The introduce of oxalate groups featuring a planar conjugated structure into the polyurethane backbone resulted in enhanced chain rigidity and increased hydrogen bond density, leading to a significant improvement in the tensile strength. In particular, PU-PHD-2k shows 44.6 MPa in tensile strength, 535.4% in elongation at break, and 159.0 kN m -1 in tear strength, which can rival and even surpass commercial polyester polyol derived polyurethanes. On the other hand, the oxalate group promotes the aggregation of the hard segments, thereby enhanced the microphase separation. Atomic force microscopy (AFM) and fourier transform infrared spectroscopy (FT-IR) results demonstrate that PU-PHD's superior mechanical properties are attributed to the ordered hydrogen bonds formed in its structure and significant microphase separation between the soft and hard segments. Moreover, PU-PDD demonstrates excellent mechanical strength and degradability, making it potentially suitable for biomedical and agricultural applications. • Polyoxalate diols are synthesized from dimethyl oxalate - an abundant and cost-effective raw material - with a highly efficient catalyst used at low loadings, enabling the transformation of coal resources into high value-added chemicals. • The molecular structure and physical properties of novel polyoxalate diols are determined systematically. • With exceptional mechanical properties, polyoxalate-based polyurethanes can match and even outperform commercial polyurethanes in terms of tensile and tearing strength.

  PublisherDOI

• Assessing bioavailability and the toxicity of resveratrol nanoparticles: Insights from an in vivo chicken embryonic model

  Food and Chemical Toxicology · 2025-03-14

  article
  PublisherDOI

• Author response for "Biligand synergistic MOFs with dual enhancements in stability and charge transfer for efficient CO2 photoreduction"

  2025-11-18

  peer-review
  PublisherDOI

• Application of convolutional neural network in the production and processing of flower and fruit tea

  Journal of Future Foods · 2025-06-18 · 2 citations

  articleOpen access

  • CNN reduces feature extraction time • CNN outperforms traditional and machine learning algorithms • The application of CNN promotes the transformation of the tea industry towards intelligence • Provide theoretical guidance for the sustainable development of the tea industry At present, the global tea industry is in a stage of transformation towards intelligent chemical development. Although traditional machine learning methods have achieved good results in the production and processing supervision of flower and fruit tea, it is difficult to improve supervision efficiency due to the limitations of manually extracting features. The automatic feature learning function of convolutional neural networks(CNN) solves this limitation and opens up a new perspective for the intelligent development of the flower and fruit tea industry. This article reviews the latest progress in the application advantages of CNN in the flower and fruit tea industry. A systematic review and meta-analysis were conducted on applying CNN in pest control, harvesting, and processing methods of flower and fruit tea raw materials (teas, flowers, fruits). Finally, an outlook was made on the relevant advanced progress and prospects. Compared with traditional machine learning methods, CNN has significant advantages in supervising flower and fruit tea production and processing. This review is expected to provide new insights into the application of intelligent technology in the tea industry.

  PublisherDOI

• Effect of different polyphenols on the stability and digestibility of multilayer emulsions fabricated from polyphenol-protein covalent complexes

  Journal of Food Measurement & Characterization · 2025-06-19 · 5 citations

  article
  PublisherDOI

• Advanced spectroscopic, microscopic, and compositional techniques in catalytic material characterization: Applications and progress

  Nano Research · 2025-07-18

  articleOpen access

  Catalytic reactions play a key role in energy production, green chemistry and chemical synthesis, and are the cornerstone for addressing global challenges such as environmental pollution and energy crisis. The design and performance optimization of efficient catalysts rely on a deep understanding of their structural characteristics, electronic states and kinetic behaviors during reactions, and advanced characterization techniques provide key technical support. This review summarizes the applications, advantages and limitations of spectroscopic techniques (X-ray absorption spectroscopy, Nuclear magnetic resonance, Raman spectroscopy, Infrared spectroscopy and Electron paramagnetic resonance), Microscopic imaging techniques (Transmission electron microscopy, Scanning electron microscopy and Atomic force microscopy) and component analysis techniques (X-ray photoelectron spectroscopy, X-ray diffraction and Inductively coupled plasma mass spectrometry) in catalytic research. These techniques can provide multi-dimensional insights into the microstructure of catalysts, the properties of active sites and their evolution during reactions, laying a solid foundation for elucidating catalytic mechanisms and optimizing catalyst performance. Although current characterization methods still face challenges in spatial resolution, compatibility with extreme reaction conditions and data processing complexity, significant progress is expected through emerging strategies such as multi-technique integration and artificial intelligence-assisted analysis. This review aims to provide a reference for researchers in the field of catalysis and a forward-looking perspective for the development of characterization techniques.

  PublisherOA PDFDOI

• Straw retention combined with phosphorus application improved soil properties, root nitrogen metabolism and optimized the relationship between root and shoot of cotton

  Research Square · 2025-04-14

  preprintOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Multilayered Regulation of Fungal Phosphate Metabolism: From Molecular Mechanisms to Ecological Roles in the Global Phosphorus Cycle

  Life · 2025-10-28 · 5 citations

  reviewOpen access

  Phosphates are essential nutrients for living organisms, and they are involved in various biological processes, including lipid metabolism, energy synthesis, and signal regulation. Recent studies have elucidated the fundamental components and transport proteins of phosphate signaling pathways, thereby providing a more profound understanding of phosphate metabolism in fungi. In this review, we concentrate on synthesizing the recent findings concerning phosphate metabolism in fungi over the past five years. These findings include the role of phosphates in the global phosphorus cycle, their effect on fungal growth and development, the variations in PHO signaling pathways among different species, and their pivotal role in symbiosis with plants. A mounting body of research substantiates the notion that phosphates play a pivotal role in regulating fungal life activities through a multifaceted mechanism. This regulatory function encompasses the promotion of growth and development, adaptation to environmental variations among different fungal species, and the evolution of distinct regulatory factors and transport proteins. Consequently, this fosters fungal diversity.

  PublisherDOI

• Predictive Design of Sustainable Biobased Packaging via Machine Intelligence for Improved Postharvest Preservation

  Research Square · 2025-05-26

  preprintOpen access
  PublisherOA PDFDOI

Frequent coauthors

• Zi Teng

  University of Maryland, College Park

  45 shared

• Yaguang Luo

  Agricultural Research Service

  40 shared

• Xianggui Chen

  Xihua University

  30 shared

• Peihua Ma

  University of Maryland, College Park

  27 shared

• Yukun Huang

  Xihua University

  26 shared

• Lei Mei

  Chinese Academy of Sciences

  26 shared

• Yangchao Luo

  University of Connecticut

  22 shared

• Boce Zhang

  University of Florida

  22 shared

Labs

• Qin WangPI