About

Professor Shuming Nie is the Grainger Distinguished Chair in Engineering and a Professor of Bioengineering, Chemistry, Materials Science and Engineering, Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. He is also the Founding Dean of the College of Engineering and Applied Sciences at Nanjing University in China. His academic research primarily focuses on nanomedicine, image-guided cancer surgery, cell-based immunotherapy, wearable optoelectronic devices, and digital health. His major achievements include the discovery of colloidal metal nanoparticles that significantly amplify surface-enhanced Raman scattering (SERS), pioneering work on water-soluble semiconductor quantum dots for biomedical applications, and developing multifunctional smart nanoparticles for integrated biomedical imaging and therapy, including image-guided cancer surgery. Professor Nie has published over 300 papers, holds numerous patents, and has delivered nearly 500 invited lectures worldwide. His scholarly work has been cited more than 100,000 times, with an H-index of 113. He received his BS from Nankai University in China, and his MS and PhD from Northwestern University, with postdoctoral research at Georgia Tech and Stanford University.

Research topics

• Internal medicine
• Medicine
• Chemistry
• Nanotechnology
• Materials science

Selected publications

• Interpretable Wavelet-CNN for Accurate Serum Raman Lung Cancer Diagnosis under Leakage-Safe, Patient-Level Splits

  Analytical Chemistry · 2026-04-20

  articleCorresponding

  Clinical cancer diagnostics require ML that maintains accuracy under biological variability with interpretable feature attribution─a particular challenge for serum-based approaches where healthy and diseased samples share >95% chemical composition. Continuous wavelet transformation (CWT) combined with convolutional neural networks (CNN) has demonstrated robust classification of Raman spectra for materials under synthetic noise conditions, but whether this approach can handle biological variability in clinical samples, and which spectral features drive its predictions, has not been explored. Here, we demonstrate application of CWT-CNN deep learning to clinical disease diagnosis, analyzing spontaneous Raman spectra from a retrospective cohort of 213 patient serum samples (106 lung cancer, 107 controls) collected over 3 years. We extend the established CWT-CNN framework with interpretability analysis using Gradient-weighted Class Activation Mapping (Grad-CAM) and inverse-CWT reconstruction. Using only 5 μL of serum and 10 min of acquisition time per patient, our approach achieved 90.5% accuracy in an independent validation cohort (19/21 correct diagnoses, 91.7% sensitivity, 88.9% specificity) using strict patient-wise data splitting. Interpretability analysis revealed that classification decisions focus on Raman shifts at 1004 cm–1 (phenylalanine), 1129 cm–1 (lipid trans-conformation), 1458 cm–1 (nucleotides), and 1560 cm–1 (tryptophan). These spectral features correspond to molecules with established roles in cancer metabolism. This demonstration that CWT-CNN maintains high accuracy under biological variability and leakage-safe, patient-level validation, combined with biochemically meaningful feature attribution, establishes a data-first approach where comprehensive spectral analysis enables both diagnostic accuracy and identification of disease-relevant molecular features.

  PublisherDOI

• High‐Entropy Lead‐Free Organic–Inorganic Hybrid Perovskites Exhibiting Broad Absorption and Bright Golden Emission for LEDs

  Angewandte Chemie · 2025-11-23

  article

  Abstract Metal halide perovskite nanomaterials are attractive for optoelectronic applications due to their exceptional optoelectronic properties; however, lead toxicity and stability issues significantly limit their practical use. High‐entropy materials (HEMs) leverage multi‐principal component synergy to form configurational entropy‐stabilized solid solutions, exhibiting unique physicochemical properties and superior stability arising from atomic‐scale chemical disorder and reconstructed local electronic states. Nevertheless, their luminescence efficiency often requires improvement. Here, we report the room‐temperature synthesis of a novel organic–inorganic hybrid high‐entropy perovskite, (TEA) 2 (Zr 0.18 Te 0.22 Hf 0.2 Sn 0.3 Pt 0.1 )Cl 6 (TEA = tetraethylammonium). Exploiting the synergistic effects among five diverse B‐site cations, this material exhibits broad‐spectrum–excitable broadband emission, producing a distinct golden light. The study demonstrates that this material retains 80% of its initial photoluminescence intensity after 1 h of continuous ultraviolet irradiation and maintains 60% of its original emission intensity even when heated to 340 K. Furthermore, its facile room‐temperature synthesis facilitates promising applications, such as in light‐emitting diodes and x‐ray detection. These findings provide crucial insights for advancing the development of efficient and stable novel optoelectronic materials.

  PublisherDOI

• Abstract 2528: Bioinspired multispectral imaging for intraoperative detection of metastatic lymph nodes: enhancing precision through UV and NIR fluorescence

  Cancer Research · 2025-04-21

  article

  Intraoperative identification of metastatic lymph nodes (LNs) remains a clinical challenge, with current techniques often leading to unnecessary removal of healthy tissue or retention of cancer-positive nodes. To address this issue, we developed a bioinspired multispectral imaging system that integrates ultraviolet (UV) autofluorescence and near-infrared (NIR) fluorescence detection. This platform mimics the mantis shrimp's unique visual system, leveraging vertically stacked photodiodes and pixelated spectral filters to achieve simultaneous imaging across the UV, visible, and NIR spectral ranges without spatial misalignment or loss of resolution. The system uses indocyanine green (ICG) as an FDA-approved NIR fluorescent probe for LN localization and UV excitation to evaluate LN status via autofluorescence. The UV fluorescence leverages endogenous biomarkers, such as tryptophan, commonly enriched in tumor-associated tissues. This dual-modality imaging approach enables real-time identification of both the anatomical location and pathological status of LNs, reducing dependence on postoperative histopathological analysis. In a clinical study involving 33 breast cancer patients, the imaging system achieved a positive predictive value of 96% and a receiver operating characteristic (ROC) area under the curve (AUC) of 89% in distinguishing metastatic from non-metastatic lymph nodes. These results were corroborated by pathology, which served as the ground truth. By providing immediate and accurate feedback to surgeons, this system has the potential to improve surgical precision, reduce unnecessary excisions, and minimize the need for follow-up procedures. Unlike traditional multispectral imaging systems reliant on mechanically rotating filters or dichroic beamsplitters, our bioinspired sensor eliminates co-registration errors and significantly reduces system complexity. Its compact design and capability to perform high-resolution imaging under challenging surgical lighting conditions make it well-suited for widespread clinical adoption. Further studies are underway to validate the system’s efficacy across different cancer types and expand its applications to other surgical scenarios. This novel imaging platform offers a significant step forward in integrating real-time molecular and anatomical imaging into intraoperative workflows, addressing critical gaps in precision oncology. Citation Format: Zhongmin Zhu, Yifei Jin, Brianna Hajek, Shuming Nie, Viktor Gruev. Bioinspired multispectral imaging for intraoperative detection of metastatic lymph nodes: enhancing precision through UV and NIR fluorescence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2528.

  PublisherDOI

• Ultrathin Atomically Flat Gold Film for Scanning Tunneling Microscopy and Single-Particle Fluorescence Spectroscopy

  Langmuir · 2025-06-17 · 1 citations

  article

  To enable rear illumination (e.g., TIRF), single-particle fluorescence microscopy, and scanning tunneling microscopy (STM) on the same nanoparticle sample, we investigate the smoothness limit and the thickness limit of template-stripped gold films made with a simple room-temperature deposition protocol ranging from 1 to 200 pm/s on four common substrates: mica, fused silica, silicon, and quartz. The resulting transparent conductive gold film achieves a thickness as low as 9 nm, absorbance as low as 0.2, and a root-mean-square roughness of 80 pm over a 100 × 100 nm2 area. We further assess whether such gold films enable single-particle characterization by fluorescence imaging and STM imaging on the same sample. Carbon dots, made by a top-down method, with a height as low as 1.0 nm (∼3 layers), can be resolved clearly on the gold film island surfaces by using both atomic force microscopy and STM, and the carbon dot single-particle fluorescence blinking can be measured by confocal microscopy. In this way, both optical and electronic characterization can be enabled on the same sample using a substrate that is relatively easy to make in batches.

  PublisherDOI

• Perovskite Nanocrystal Enhanced Vertically Stacked-Photodiode Image Sensor for Wavelength Resolved UV Imaging

  2025-05-25

  article

  We introduce a novel low-power imaging system capable of multispectral ultraviolet (UV) imaging with the ability to distinguish between different UV wavelengths. This advancement is achieved by enhancing a three-layer vertically stacked photodiode (3T APS) complementary metal-oxide-semiconductor (CMOS) image sensor with specially manufactured and tuned perovskite nanocrystals (PNCs), effectively extending the sensor’s multi-channel quantum efficiency across the UVB and UVA range. Our imaging system includes all necessary peripherals and circuits. Experimental results demonstrate that the PNC-enhanced sensor can precisely differentiate 11 distinct UV wavelengths between 300 nm and 400 nm, a level of detail unachievable with the unmodified sensor. This technology offers substantial potential for industrial and clinical applications, including the in-vitro detection of multiple biomarkers with subtly different UV fluorescence emission spectra.

  PublisherDOI

• Signal-to-Noise Ratio Imaging and Real-Time Sharpening of Tumor Boundaries for Image-Guided Cancer Surgery

  Analytical Chemistry · 2025-04-07 · 4 citations

  articleSenior authorCorresponding

  Fluorescence-guided cancer surgery is of considerable current interest in bioanalytical chemistry, engineering, and medicine, but its clinical utility is still hampered by the diffusive (scattering) nature of human tissues and large variations among different patients. Here, we report a new method based on signal-to-noise (contrast-to-noise) ratio (SNR or CNR) imaging for real-time delineation and sharpening of tumor boundaries during image-guided cancer surgery. In particular, we show that in vivo tumor fluorescence signals (both intensity and standard deviation) are strongly correlated with those of the surrounding tissue of the same tissue type and that this relationship is maintained as a function of time for fluorescent tracers such as indocyanine green. This dynamic relationship permits a precise removal of nonspecific background fluorescence from tumor fluorescence. As a result, single-pixel SNR values have been calculated, mapped, and displayed across a large surgical field at 60 frames per second. Pathological validation studies indicate that these SNR values correspond to statistical confidence levels similar (but not identical) to those of normal distributions. When the tumor fluorescence has an SNR of 3, pathological data show a confidence level of approximately 95% in identifying the true tumor lesions. For clinical relevance, we have also carried out first-in-human clinical studies for both oral and esophageal tumors, achieving tumor margin precisions of 1-2 mm with 87.5% histological accuracy and no false positives.

  PublisherDOI

• Light detectors made from perovskite crystals see in full colour

  Nature · 2025-06-18

  article1st authorCorresponding
  PublisherDOI

• Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges

  ACS Applied Materials & Interfaces · 2025-02-24 · 167 citations

  reviewOpen access

  deep Raman spectroscopy, emphasizing its potential for liquid biopsy, metabolic phenotyping, and extracellular vesicle diagnostics. The review concludes with a perspective on clinical translation of SERS, addressing commercialization potentials and the challenges in deep tissue in vivo sensing and imaging.

  PublisherOA PDFDOI

• UV-Visible-NIR Image Sensor for Labeled and Label-free Intra-operative Imaging with Human Clinical Validation

  2025-05-25

  article

  We present a single-chip imaging sensor capable of simultaneous ultraviolet (UV), visible (VIS), and near-infrared (NIR) imaging. The system integrates a checkerboard-patterned multispectral pixelated filter array with a three-layer stacked photodiode architecture, achieving six distinct spectral bands. Each photodiode layer is optimized for specific wavelengths, leveraging the wavelength-dependent absorption properties of silicon. With a power consumption of 250 mW and nearly 100% transmission in both UV and NIR spectra, the platform is optimized for intraoperative use without disrupting the surgical workflow. Clinical data from 33 patients with breast cancer demonstrated a positive predictive value of 100% for detecting primary tumors based on UV autofluorescence. These results highlight the platform’s potential for enhancing cancer detection in real-time surgical settings.

  PublisherDOI

• CNN-based demosaicing for labeled fluorescence cancer intraoperative imaging with visible-NIR sensors

  2025-03-20

  article

  Single-chip imaging devices with vertically stacked photodiodes and pixelated spectral filters enhance multi-dye imaging techniques for cancer surgeries. However, this advancement sacrifices spatial resolution. To address this issue, we have created a deep convolutional neural network designed to demosaic color and NIR channels, and its effectiveness has been confirmed through testing on both preclinical and clinical datasets.

  PublisherDOI

Recent grants

• NIH Grant R01GM060562

  NIH · $805k · 2005

• NIH Grant R01CA163256

  NIH · $5.1M · 2018

• NIH Grant R01GM058173

  NIH · $819k · 2005

• NIH Grant R01CA108468

  NIH · $6.9M · 2011

• NIH Grant RC2CA148265

  NIH · $2.0M · 2013

Frequent coauthors

• Andrew M. Smith

  University of Illinois Urbana-Champaign

  99 shared

• Xiaohu Gao

  East China Jiaotong University

  80 shared

• May D. Wang

  56 shared

• Yiqing Wang

  Nanjing University

  55 shared

• Nai‐Teng Yu

  Microscan Systems (United States)

  52 shared

• Sunil Singhal

  49 shared

• Ximei Qian

  Emory University

  43 shared

• Amit Agrawal

  All India Institute of Medical Sciences Bhopal

  41 shared

Awards & honors

• Grainger Distinguished Chair in Engineering, University of I…
• Fellow of AAAS
• Fellow of AIMBE
• Fellow of IAMBE