About

Wenjun (Rebecca) Cai is an associate professor in the Materials Science and Engineering department at Virginia Tech. Her research interests include physical metallurgy, tribology, corrosion, and tribocorrosion, with a focus on materials deformation and degradation under extreme conditions. She is involved in materials characterization, mechanical testing, and fracture mechanics, as well as the study of thin films and coatings. Dr. Cai received her B.S. in Materials Science from Fudan University in China in 2005 and her Ph.D. in Materials Science Engineering from the University of Illinois at Urbana-Champaign in 2010. Her notable honors include the 2017 TMS Young Leaders Professional Development Award, the 2016 Outstanding Faculty Award from the University of South Florida, the 2015 NSF CAREER Award, and the 2010 Racheff-Intel Award for Outstanding Graduate Research. She is affiliated with professional organizations such as the Minerals, Metals & Materials Society (TMS), Materials Research Society (MRS), and ASM International.

Research topics

• Composite material
• Materials science
• Metallurgy
• Computer Science
• Geometry
• Biology
• Neuroscience
• Optics

Selected publications

• Correction: Materials laboratories of the future for alloys, amorphous, and composite materials

  MRS Bulletin · 2025-02-28

  articleOpen access
  PublisherOA PDFDOI

• Reconfigured continuous normalized gradient flow for computing ground states of Bose-Einstein condensates

  Computer Physics Communications · 2025-01-17 · 2 citations

  article
  PublisherDOI

• The application prospect of metal/metal oxide nanoparticles in the treatment of intervertebral disc degeneration

  Nanoscale · 2025-01-01 · 1 citations

  review

  With advancements in molecular biology and tissue engineering, significant progress has been made in the treatment of intervertebral disc degeneration (IVDD). In recent years, biomaterials have broadened therapeutic options for IVDD, particularly through the incorporation of metals, which impart antioxidant, anti-inflammatory, and cellular repair properties. The combination of metal ions, nanomaterials, and bioactive molecules further enhances the capacity of these materials to scavenge free radicals, regulate cell activity, and improve the microenvironment, thereby increasing their therapeutic efficacy and providing new opportunities for IVDD treatment. This review aims to provide a comprehensive analysis of the roles of metal-based and metal oxide nanoparticles in the treatment of IVDD, while addressing current challenges and future prospects related to their therapeutic applications.

  PublisherDOI

• Microstructure-agnostic deep learning for mechanistic discovery of corrosion-resistant Co-Cr-Fe-Ni MPEAs

  Acta Materialia · 2025-10-09 · 4 citations

  articleSenior authorCorresponding
  PublisherDOI

• Materials laboratories of the future for alloys, amorphous, and composite materials

  MRS Bulletin · 2025-01-29 · 4 citations

  articleOpen access

  Abstract In alignment with the Materials Genome Initiative and as the product of a workshop sponsored by the US National Science Foundation, we define a vision for materials laboratories of the future in alloys, amorphous materials, and composite materials; chart a roadmap for realizing this vision; identify technical bottlenecks and barriers to access; and propose pathways to equitable and democratic access to integrated toolsets in a manner that addresses urgent societal needs, accelerates technological innovation, and enhances manufacturing competitiveness. Spanning three important materials classes, this article summarizes the areas of alignment and unifying themes, distinctive needs of different materials research communities, key science drivers that cannot be accomplished within the capabilities of current materials laboratories, and open questions that need further community input. Here, we provide a broader context for the workshop, synopsize the salient findings, outline a shared vision for democratizing access and accelerating materials discovery, highlight some case studies across the three different materials classes, and identify significant issues that need further discussion. Graphical abstract

  PublisherOA PDFDOI

• Message from the Editorial Chairwoman

  Wear · 2025-03-17

  article1st authorCorresponding
  PublisherDOI

• Reconfigured continuous normalized accelerated gradient flow for computing ground states of spin-1 Bose–Einstein condensates

  Communications in Nonlinear Science and Numerical Simulation · 2025-07-01 · 1 citations

  article
  PublisherDOI

• Differential distribution of characteristic constituents in peel and pulp of Aurantii Fructus Immaturus (Citrus aurantium L.) using MALDI mass spectrometry imaging

  Fitoterapia · 2024-06-08 · 5 citations

  articleOpen access1st author

  Aurantii Fructus Immaturus (AFI) was structurally divided into two parts named "peel" and "pulp". The exocarp and mesocarp of materials named "peel". The endocarp separated into multiple compartments and the cystic hair attached to it named "pulp". In order to explore the distribution and content of constituents in AFI, an efficient method to explore the distribution of constituents was developed based on matrix assisted laser desorption/ionization fourier transform ion cyclotron resonance mass spectrometry imaging (MALDI-FTICR-MSI). After simple processing, thirty-two constituents with distinct localization in the mass range of 101-1200 Da were identified by MALDI-FTICR-MSI. In addition, the identified four flavnoids (poncirin, sinensetin, 3,5,6,7,8,3',4'-heptemthoxyflavone, and tangeritin) were analyzed for differences between using LC-MS. Quantitative analysis results supported the quantitative results from MALDI-FT-ICR-MSI. The results implied that different parts had different constituents in AFI, and demonstrated MALDI-MSI have high potential in the direct analysis of constituents.

  PublisherDOI

• Study on Rock Mechanics Parameter Prediction Method Based on DTW Similarity and Machine-Learning Algorithms

  Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description · 2024-02-01 · 2 citations

  article1st authorCorresponding

  Rock mechanics parameters are crucial factors for predicting rock behavior in oil and gas reservoirs, optimizing extraction strategies, and ensuring drilling safety. In this study, we propose a random forest (RF)-convolutional neural network (CNN)-long-term short-term memory network (LSTM) fusion model based on the dynamic time warping (DTW) algorithm to construct intelligent prediction models for elastic modulus, Poisson’s ratio, and compressive strength using real-time drilling engineering data. An autoencoder with a sliding window is employed to automatically identify abnormal points or segments in the calculated values of elastic modulus, Poisson’s ratio, and compressive strength obtained from drilled wells. These abnormal values are then corrected using a backpropagation (BP) neural network. Compared to single CNN-LSTM or single RF models, the RF-CNN-LSTM fusion model performs better. It achieves this by effectively combining the strengths of different algorithms in predicting outcomes. The accuracy of the RF-CNN-LSTM fusion model is over 94% when compared to the actual values. Furthermore, the analysis of the relative importance of input parameters reveals that weight on bit (WOB), temperature, displacement, equivalent circulation density (ECD), and mud density are the primary input features for predicting elastic modulus. For predicting Poisson’s ratio, the main input features include WOB, mud density, ECD, temperature, pumping pressure, displacement, and rate of penetration (ROP). Similarly, for predicting compressive strength, the main input features consist of WOB, temperature, displacement, ECD, and mud density. The research findings demonstrate that the rock mechanics parameter prediction models based on the RF-CNN-LSTM algorithm using DTW exhibit high computational accuracy in the B oil field of China. These results are significant for gaining a deeper understanding of the variations in rock mechanics parameters and optimizing drilling decisions.

  PublisherDOI

• Carbon nanofibers embedded with Fe–Co alloy nanoparticles via electrospinning as lightweight high‐performance electromagnetic wave absorbers

  Rare Metals · 2024-02-28 · 91 citations

  article1st authorCorresponding

  Abstract As a lot of electromagnetic pollution and interference issues have emerged, to overcome electromagnetic interference, prevent electromagnetic hazards, and develop new high‐performance electromagnetic wave (EMW) absorbers have become a significant task in the field of materials science. In this paper, a three‐dimensional (3D) carbon nanofibers network with core–shell structure, embedded with varied molar ratios of iron and cobalt (4:0, 3:1, 2:2, 1:3, 0:4), was effectively synthesized (Fe/Co@C‐CNFs) via electrospinning. The phase, microstructure, magnetic and EMW absorption properties were studied. It is discovered that Fe/Co@C‐CNFs doped with iron: cobalt = 1:1 have excellent EMW absorption capacity. When the matching thickness is 1.08 mm, the minimum reflection loss (RL) value is − 18.66 dB, while the maximum effective absorption bandwidth (EAB) reaches 4.2 GHz (13.9–18 GHz) at a thickness of 1.22 mm. This is owing to the absorbers' superior impedance matching and multiple reflections as well as the conductivity, dielectric, and magnetic losses of carbon nanofibers embedded with Fe–Co alloy particles. In addition, the radar cross section (RCS) of the absorbers has been calculated by CST Studio Suite, showing that the absorbing coating can effectively reduce the RCS at various angles, especially for Fe/Co@C‐CNFs doped with iron:cobalt = 1:1. These findings not only provide new insights for the preparation of lightweight and high‐performance electromagnetic wave absorbers, but also contribute to energy storage and conversion.

  PublisherDOI

Recent grants

• CAREER: Effects of Alloy Concentration on the Tribocorrosion Resistance of Al-TM Supersaturated Solid Solutions

  NSF · $312k · 2018–2021

• Collaborative Research: Far-from-equilibrium surfaces of high entropy alloys: interplay between frictional sliding and corrosion damage

  NSF · $436k · 2021–2025

• CAREER: Effects of Alloy Concentration on the Tribocorrosion Resistance of Al-TM Supersaturated Solid Solutions

  NSF · $416k · 2015–2018

• Optimizing Wear and Corrosion Resistance of Superlattice Coatings through Atomic-Scale Design

  NSF · $430k · 2018–2021

Frequent coauthors

• Jia Chen

  16 shared

• Kaiwen Wang

  Ocean University of China

  12 shared

• Hesham Mraied

  University of South Florida

  12 shared

• Wenbo Wang

  Beihang University

  10 shared

• Lin Li

  Henan Normal University

  9 shared

• Rong Luo

  Southern Medical University

  8 shared

• Pascal Bellon

  University of Illinois Urbana-Champaign

  8 shared

• Zhengyu Zhang

  Anhui University

  7 shared

Awards & honors

• 2017 TMS Young Leaders Professional Development Award
• 2016 Outstanding Faculty Award, University of South Florida
• 2015 National Science Foundation CAREER Award
• 2010 Racheff-Intel Award for Outstanding Graduate Research,…