About

VT MEMS Lab, established in 2005, has been pursuing research in MEMS, nanotechnology, and Microfluidics (MnM) to develop highly innovative miniaturized analyzers for chemical and biomedical applications. Two major research thrusts in the lab are Micro Analytical Chemistry (MAC) and BioMEMS/NEMS (Bio).

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Chemistry
• Materials science
• Optics
• Chromatography
• Nanotechnology
• Optoelectronics
• Internal medicine
• Medicine
• Engineering
• Biological system
• Process engineering
• Biology
• Physics

Selected publications

• Cold-Spot-Free Interconnects in Modular Micro Gas Chromatography: A breakthrough enabling C30 elution

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Cold-spot-free interconnects in modular micro gas chromatography: a breakthrough enabling C30 elution

  Microchemical Journal · 2026-04-03

  articleSenior author
  PublisherDOI

• Fluidic and Electrical Modular Interfacing: A modular approach to micro total analytical systems and micro gas chromatography

  Sensors and Actuators B Chemical · 2025-07-07 · 6 citations

  articleSenior authorCorresponding
  PublisherDOI

• Monitoring Skin Volatile Emissions Using Wearable Sensors

  Annual Review of Analytical Chemistry · 2025-05-15 · 1 citations

  reviewOpen access

  Human skin emits a continuous flux of volatile compounds reflecting various metabolic processes in the body, microbial activity, and environmental factors. Harnessing this emission for diagnostics is of great interest given the noninvasive, passive, and accessible nature of the emission, and there is much research underway to understand the value of this skin-emitted volatile organic compound (VOC) matrix. In parallel to this, wearable skin VOC sensors are emerging and garnering attention due to their potential to provide noninvasive, real-time information for monitoring human health, overcoming many of the design challenges related to biofluid monitoring via wearables. The projected opportunities for skin VOCs are fueling innovations in wearable VOC monitoring. This review discusses the most recent developments, from fully integrated wearable skin VOC sensors that exploit existing semiconductor technology to the design and preparation of advanced new sensing materials and devices to deliver new modalities for wearable skin VOC sensors. We articulate the challenges, limitations, and opportunities for technological advances to provide a perspective on promising directions for future developments.

  PublisherDOI

• Nonparametric Bayesian functional clustering with applications to racial disparities in breast cancer

  Statistical Analysis and Data Mining The ASA Data Science Journal · 2024-01-25 · 1 citations

  articleOpen accessSenior author

  Abstract As we have easier access to massive data sets, functional analyses have gained more interest. However, such data sets often contain large heterogeneities, noises, and dimensionalities. When generalizing the analyses from vectors to functions, classical methods might not work directly. This paper considers noisy information reduction in functional analyses from two perspectives: functional clustering to group similar observations and thus reduce the sample size and functional variable selection to reduce the dimensionality. The complicated data structures and relations can be easily modeled by a Bayesian hierarchical model due to its flexibility. Hence, this paper proposes a nonparametric Bayesian functional clustering and peak point selection method via weighted Dirichlet process mixture (WDPM) modeling that automatically clusters and provides accurate estimations, together with conditional Laplace prior, which is a conjugate variable selection prior. The proposed method is named WDPM‐VS for short, and is able to simultaneously perform the following tasks: (1) Automatic cluster without specifying the number of clusters or cluster centers beforehand; (2) Cluster for heterogeneously behaved functions; (3) Select vibrational peak points; and (4) Reduce noisy information from the two perspectives: sample size and dimensionality. The method will greatly outperform its comparison methods in root mean squared errors. Based on this proposed method, we are able to identify biological factors that can explain the breast cancer racial disparities.

  PublisherOA PDFDOI

• Miniaturized Gas Chromatographic Nose for On-Site Adulteration Detection

  IEEE Transactions on Instrumentation and Measurement · 2023-01-01 · 11 citations

  articleSenior author

  Miniaturized gas chromatographs (μGCs) have been applied to a diverse set of applications bringing their analytical capabilities to the field and point of care solutions. This work presents, for the first time, the use of a μGC for the rapid detection of fuel adulteration using pattern recognition techniques. The new μGC which weighs around 2 kg and has dimensions of 25 cm X 22 cm X 15 cm houses two 1m-long semi-packed columns fabricated using microelectromechanical systems (MEMS) technology and utilizes two commercial miniature photoionization detectors (PI-Ds). The columns are coated with two different ionic liquids as their stationary phases. All the electrical and fluidic components are controlled by stackable, modular electronics specifically designed and implemented for multi-channel μGCs. The current electronics can support six valves/pumps, four microcolumns/micro preconcentrators with temperature programming and five commercial photoionization detectors. The modular electronics is designed to be expandable and to accommodate the integration of more GC columns enabling more complex analysis. The efficiency of this portable GC nose is demonstrated by discriminating between a pure diesel sample and the one altered by adding 10% kerosene. Without the need for full chromatographic separation, the patterns generated by the two columns were able to identify all the samples accurately. This portable GC nose can be used in a wide variety of applications in which a binary response is needed for rapid screening.

  PublisherDOI

• A MEMS-enabled portable gas chromatography injection system for trace analysis

  Analytica Chimica Acta · 2023-04-19 · 8 citations

  articleSenior authorCorresponding
  PublisherDOI

• Electronic Raman scattering calibration for quantitative surface-enhanced Raman spectroscopy and improved biostatistical analysis

  2022-05-27

  article

  Despite its ultrasensitive detection capability, surface-enhanced Raman spectroscopy (SERS) faces challenges as a quantitative biochemical analysis due to the significant dependence of local field enhancement on nanoscale geometric variations. Significant efforts have been devoted to develop SERS calibration methods by introducing Raman tags as internal standards. Raman tags undergo similar SERS enhancement, and ratiometric signals for target analytes can be generated with reduced SERS enhancement variations. However, using Raman tags still faces challenges for label-free applications, including spatial competition between the analyte and tags in hotspots, spectral interference, limited long-term stability due to laser-induced photo-degradation. We demonstrate that electronic Raman scattering (ERS) signals from metallic nanostructures at hotspots can serve as the internal calibration standard to enable quantitative SERS analysis and improve biostatistical analysis. ERS is omnipresent in any plasmonic construct and shown as a broad continuous background in SERS measurements. Both ERS and SERS processes experience the |E|4 local enhancements during the excitation and inelastic scattering transitions. We demonstrate that ERS-calibrated SERS signals are insensitive to variations from different hotspots and thus can enable more accurate quantitative SERS analysis. For validation, we performed label-free SERS analysis of living biological systems using four different cancer cell lines cultured on SERS devices and their drug responses. Remarkably, after ERS calibration, the statistical scatter plots are more similar to the intrinsic biological properties of cancer subtype categorization and their known drug responses. Therefore, we envision that ERS calibrated SERS can find crucial opportunities in label-free molecular profiling of complicated biological systems.

  PublisherDOI

• Nanolaminate Plasmonic Substrates for High-Throughput Living Cell SERS Measurements and Artificial Neural Network Classification of Cellular Drug Responses

  ACS Applied Nano Materials · 2022-07-26 · 29 citations

  article

  Rapid in situ bio-analysis of cellular behaviors in response to external stimuli remains a formidable challenge but can open crucial opportunities in biology and medicine. The standard label-based end point assays suffer from invasiveness and complex sample handling. In this regard, label-free surface-enhanced Raman spectroscopy (SERS) has emerged as a promising non-invasive in situ bio-analysis technique for living cells. Nevertheless, achieving rapid in situ SERS bio-analysis still faces challenges in reliable high-throughput measurements and accurate multivariate analysis, which requires significant innovations in bio-interfaced SERS devices and machine learning (ML) methods. Here, we exploit cell-interfaced nanolaminate SERS substrates to demonstrate reliable high-throughput SERS measurements using well-studied living cancer cells with four drug dosages. Artificial neural network (ANN) for multiclass classification of cellular drug responses provides high accuracy (94%). Uniquely, nanolaminate SERS substrates with a high SERS enhancement factor (>107) can rapidly generate big SERS data sets with rich molecular information on living cells (10,000 spectra within 3 min) that can enable the utilization of data-hungry ML methods (e.g., ANN). By capturing additional hidden features in high-dimensional spectroscopic data, ANN is more powerful for multiclass classification than five other popular ML methods, including principal component analysis combined with linear discriminant analysis (PCA-LDA), partial least-squares discriminant analysis (PLSDA), classification tree (CT), k-nearest neighbor (KNN), and support vector machine (SVM). On the basis of the proof-of-concept demonstration using drugs on living cells, we anticipate that the nanolaminate SERS substrates can potentially monitor living cell responses to other external stimuli in a label-free and non-invasive manner.

  PublisherDOI

• Plasmonically Calibrated Label-Free Surface-Enhanced Raman Spectroscopy for Improved Multivariate Analysis of Living Cells in Cancer Subtyping and Drug Testing

  Analytical Chemistry · 2021 · 43 citations

  • Computer Science
  • Chemistry
  • Nanotechnology

  Plasmonic nanostructure-enabled label-free surface-enhanced Raman spectroscopy (SERS) emerges as a rapid nondestructive molecular fingerprint characterization technique for complex biological samples. However, label-free SERS bioanalysis faces challenges in reliability and reproducibility due to SERS signals' high susceptibility to local optical field variations at plasmonic hotspots, which can bias correlations between the measured spectroscopic features and the actual molecular concentration profiles of complex biochemical matrices. Herein, we report that plasmonically enhanced electronic Raman scattering (ERS) signals from metal nanostructures can serve as a SERS calibration internal standard to improve multivariate analysis of living biological systems. Through side-by-side comparisons with noncalibrated SERS datasets, we demonstrate that the ERS-based SERS calibration can enhance supervised learning classification of label-free living cell SERS spectra in (1) subtyping breast cancer cells with different degrees of malignancy and (2) assessing cancer cells' drug responses at different dosages. Notably, the ERS-based SERS calibration has the advantages of excellent photostability under laser excitation, no spectral interference with biomolecule Raman signatures, and no occupation competition with biomolecules at hotspots. Therefore, we envision that the ERS-based SERS calibration can significantly boost the multivariate analysis performance in label-free SERS measurements of living biological systems and other complex biochemical matrices.

  PublisherDOI

Recent grants

• Three Dimensional, Passivated-Electrode, Insulator-Based Dielectrophoresis (3D-PiDEP)

  NSF · $353k · 2013–2017

• High Throughput Mechanical Modulatory Assay for Breast Cancer Drug Testing

  NIH · $395k · 2016–2019

• NIH Grant R21OH010330

  NIH · $418k · 2015

• GOALI: MEMS-Based Preconcentrators with Nano-Structured Adsorbents for Micro Gas Chromatography

  NSF · $361k · 2009–2013

• MEMS-Based Multicapillary Columns with Nano-Structured Stationary Phases for High-Speed, High-Performance Gas Chromatography

  NSF · $252k · 2006–2010

Frequent coauthors

• Bassam Alfeeli

  44 shared

• Jeannine S. Strobl

  Virginia Tech

  42 shared

• Mehdi Nikkhah

  Arizona State University

  22 shared

• Richard Sacks

  University of Michigan–Ann Arbor

  21 shared

• Gary W. Rice

  William & Mary

  20 shared

• Hamza Shakeel

  Queen's University Belfast

  19 shared

• Shree Narayanan

  E.G.S. Pillay Engineering College

  18 shared

• Hesam Babahosseini

  National Institute of Biomedical Imaging and Bioengineering

  17 shared

Education

• Ph.D., Electrical Engineering

  University of California, Berkeley

  1990

• M.S., Electrical Engineering

  University of California, Berkeley

  1986

• B.S., Electrical Engineering

  University of Tehran

  1983