About

Oara Neumann is a Research Scientist in the Department of Electrical & Computer Engineering. She earned her PhD in Applied Physics from Rice University in 2013. Prior to that, she obtained an MS in Chemical Physics from the Weizmann Institute of Science in Rehovot, Israel in 2004, an MS in Analytical Chemistry from the University of Bucharest, Romania in 1997, and a BA in Chemistry from the University of Bucharest, Romania in 1996. Her research focuses on probing biomolecules using plasmonic nanostructures and on solar steam generation using plasmonic nanoparticles.

Research topics

• Materials science
• Nanotechnology
• Optoelectronics
• Nuclear engineering
• Physics
• Composite material
• Environmental science
• Chromatography
• Chemical engineering
• Engineering
• Thermodynamics
• Environmental engineering
• Chemistry
• Electrical engineering

Selected publications

• In silico machine learning–enabled detection of polycyclic aromatic hydrocarbons from contaminated soil

  Proceedings of the National Academy of Sciences · 2025-05-08 · 4 citations

  articleOpen access

  The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their modified derivatives in contaminated soil is challenging due to the chemical and microbial complexity of soil organic matter. To address these challenges, we developed an innovative analytical approach that combines Surface-enhanced Raman spectroscopy with a Raman spectral library constructed in silico using density functional theory (DFT)-calculated spectra. This method overcomes several limitations associated with traditional experimental libraries, including spectral background interference, solvent effects, and commercially unavailable or challenging to synthesize compounds. Our methodology employs a physics-informed machine learning pipeline that operates in two stages: the characteristic peak extraction (CaPE) algorithm, which isolates distinctive spectral features, and the characteristic peak similarity (CaPSim) algorithm, which identifies analytes with high robustness to spectral shifts and amplitude variations. Validation of this approach showed strong similarity values (>0.6) between DFT-calculated and experimental Surface-enhanced Raman spectra for multiple PAHs, confirming its accuracy and discriminative capability. This study establishes the viability of DFT-calculated spectra as reliable references for identifying analytes that lack experimental reference spectra, including those formed through environmental modification of PAHs. This advancement addresses a critical gap in environmental monitoring, providing a valuable tool for assessing public health risks associated with these contaminants.

  PublisherDOI

• Enhancing polyphenol delivery systems for effective chronic kidney disease management

  2025-01-01

  articleSenior author

  Nature provided the inspiration and the ingredients for therapeutic medicine. More than 50% of all current medicinal drugs are made from herbal plants, roots, and fruits. According to the World Health Organization, 75% of the world's population uses plant-based medicine. These products contain only purified ingredients that may reduce the final product's antioxidant, antibacterial, and antimicrobial properties. Therefore, we aim to develop a method of extraction that may stabilize the antioxidant properties of the product. Polyphenols are organic compounds found in fruits and vegetables such as onions, grapes, berries, cherries, broccoli, and citrus. Polyphenols exhibit significant antibacterial and antimicrobial activity, antihypertensive and vasodilator effects, and antihypercholesterolemic and antiatherosclerotic activities. Conventional extraction methods, such as ultrasound-assisted extraction, solvent extraction, supercritical fluid extraction, or microwave-assisted extraction, use solvents with different polarities (ethanol, methanol, water, and chloroform) or elevated temperatures. Here, we developed a method of extraction and stabilization that involves an alcoholic solution and plant-based stabilizers.

  PublisherDOI

• SERS and SEIRA Detection of Polycyclic Aromatic Hydrocarbons and Their Derivatives in Murine Tissues: Bioaccumulation and Clearance

  Journal of Raman Spectroscopy · 2025-10-09

  article1st author

  ABSTRACT The toxicological effects of polycyclic aromatic hydrocarbons (PAHs) and polycyclic aromatic compounds (PACs) have attracted considerable research interest due to their known bioaccumulation. However, time‐resolved and longitudinal studies that examine in vivo bioaccumulation and clearance remain limited. Here, we use surface‐enhanced Raman scattering (SERS) and surface‐enhanced infrared absorption (SEIRA) spectroscopies to detect and track the presence of two PAHs (pyrene and benzo[ a ]pyrene) and one PAC (5,12‐naphthacenequinone), in murine organ tissues. Mice were exposed to a three‐component mixture of these three chemicals; then, their organs were collected at 24, 48, and 72 h post exposure. The accumulation of each of these chemicals in the brain, liver, kidneys, heart, and spleen over a 72‐h postexposure period was determined by SERS and SEIRA and independently quantified by gas chromatography–mass spectrometry (GC–MS) analysis. The liver and kidneys displayed the highest level of accumulation, reflecting their important roles in detoxification, while significant levels were detected in the brain, indicating permeability of the blood–brain barrier and the potential for neurotoxic effects. Benzo[ a ]pyrene showed the highest retention and slowest clearance, while 5,12‐naphthacenequinone showed surprisingly minimal bioaccumulation. The independent detection of benzo[ a ]pyrene‐induced DNA adducts in multiple organs also highlights its mutagenic and carcinogenic risk.

  PublisherDOI

• Machine learning–enhanced surface-enhanced spectroscopic detection of polycyclic aromatic hydrocarbons in the human placenta

  Proceedings of the National Academy of Sciences · 2025-02-10 · 4 citations

  articleOpen access1st author

  The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their derivatives, polycyclic aromatic compounds (PACs), are essential for environmental and health monitoring, for assessing toxicological exposure and their associated health risks. PAHs/PACs are the most dangerous chemicals found in tobacco smoke, and cigarette use during pregnancy can convey these molecules to the developing fetus through the placenta. This exposure is associated with many negative health outcomes, from premature birth to sudden infant death syndrome and adverse neurodevelopmental disorders. This study demonstrates the use of surface-enhanced Raman and surface-enhanced infrared absorption spectroscopies for direct detection of PAHs/PACs in human placental tissue. We applied two spectroscopy-informed machine learning algorithms, Characteristic Peak Extraction (CaPE) and Characteristic Peak Similarity (CaPSim), to identify the specific PAHs and PACs present in the placenta of women who smoked tobacco cigarettes in pregnancy compared to spectra of the placenta from self-reported nonsmokers. CaPE and CaPSim analysis enabled a clear distinction between these two groups. Independent verification was accomplished by detecting PAH-DNA and PAC-DNA adducts in the smoking group by means of a 32 P-postlabeling assay. These findings highlight the effectiveness of combining surface-enhanced spectroscopies with informed ML analysis for the streamlined detection of hazardous environmental compounds in human tissues, suggesting broader applications in clinical diagnostics and public health surveillance.

  PublisherOA PDFDOI

• Combined Sustainable Water Purification and Remineralization Using an Off-Grid, Nanoparticle-Driven Solar Process

  ACS Sustainable Resource Management · 2025-08-28

  articleOpen access1st authorCorresponding

  The increasing demand for potable water worldwide necessitates scalable and sustainable approaches to water purification. Here, we demonstrate a solar-driven water purification system that combines nanoparticle-assisted, membrane-free solar distillation with remineralization through a natural sedimentary rock-filled condensation column. The solar distillation is accomplished using carbon black nanoparticles (CBNPs), whose broadband light absorption properties significantly enhance the evaporation rate during distillation. This is paired with a condensation-remineralization column incorporating sedimentary rocks, which effectively replenishes essential minerals such as calcium and magnesium (required for sustainable human consumption) and re-establishes water alkalinity. It achieves a 99% reduction of dissolved ions and restores calcium concentration to levels comparable to the World Health Organization (WHO) standards while also achieving effective microbial decontamination. Combining distillation and remineralization in one simple, low-tech system is a strategy that addresses the urgent demand for sustainably safe drinking water needed in many resource-limited communities and locations.

  PublisherOA PDFDOI

• Routes to Optimizing Photothermal Cancer Therapy through a Comprehensive Theoretical Model

  ACS Photonics · 2024-06-26 · 7 citations

  article

  Nanoparticle-assisted photothermal therapy is a soft tissue ablation method where nanoparticles embedded in tissue absorb near-infrared light, converting it to heat and raising temperatures directly where the nanoparticles are located, with minimal damage to adjacent tissue. Developing a method that accurately predicts light and heat dissipation within both pristine and nanoparticle-embedded tissues is essential for making this process as efficient as possible. Here, we report a theoretical model of nanoparticle-assisted photothermal therapy that provides a deeper understanding of this process. The model considers light scattering and absorption by tissue and nanoparticles, resulting in heat generation and dissipation. We find that the thermal response is a consequence of two competing processes, increased light absorption and backscattering, both dependent on nanoparticle concentration, and we accurately account for temperature-dependent tissue properties. Through this approach, we found that spatial and temporal modulation of the laser intensity, combined with heat localization, can dramatically increase the efficiency of the ablation process, resulting in a 44% greater ablation volume in 33% less illumination time. Our critical examination of this therapeutic approach through a robust, data-validated model offers valuable insights into practical strategies for potentially game-changing treatment optimization.

  PublisherDOI

• Surface-Enhanced Raman Spectroscopy: from the Few-Analyte Limit to Hot-Spot Saturation

  The Journal of Physical Chemistry C · 2024-05-16 · 9 citations

  article1st author

  Surface-enhanced Raman spectroscopy (SERS) gained much attention following initial claims and subsequent verifications of single-molecule sensitivity. SERS substrates based on plasmonic nanoparticles in close proximity create “hot spots” when illuminated, which, in the single-molecule limit, follow log–normal statistics for molecular occupancy. Here, we rigorously examine the transition from the single-molecule limit to the limit of hot spot saturation, a regime that follows Gaussian statistics, by varying a 1:1 bianalyte concentration over 3 orders of magnitude. The bianalyte model is extended here to follow this transition, and the electromagnetic “hot spots” of both Au nanoparticle and Au nanoshells-based SERS substrates are described theoretically. This combined experimental-theoretical study provides a rigorous foundation for quantifying trace analyte detection over a wider and highly practical concentration range.

  PublisherDOI

• Identifying Surface-Enhanced Raman Spectra with a Raman Library Using Machine Learning

  ACS Nano · 2023-11-01 · 50 citations

  article

  Since its discovery, surface-enhanced Raman spectroscopy (SERS) has shown outstanding promise of identifying trace amounts of unknown molecules in rapid, portable formats. However, the many different types of nanoparticles or nanostructured metallic SERS substrates created over the past few decades show substantial variability in the SERS spectra they provide. These inconsistencies have even raised speculation that substrate-specific SERS spectral libraries must be compiled for practical use of this type of spectroscopy. Here, we report a machine learning (ML) algorithm that can identify chemicals by matching their SERS spectra to those of a standard Raman spectral library. We use an approach analogous to facial recognition that utilizes feature extraction in the presence of multiple nuisance variables for spectral recognition. The key element is a metric we call "Characteristic Peak Similarity" (CaPSim) that focuses on the characteristic peaks in the SERS spectra. It has the flexibility to accommodate substrate-specific variability when quantifying the degree of similarity to a Raman spectrum. Analysis shows that CaPSim substantially outperforms existing spectral matching algorithms in terms of accuracy. This ML-based approach could greatly facilitate the spectroscopic identification of molecules in fieldable SERS applications.

  PublisherDOI

• Combined Surface-Enhanced Raman and Infrared Absorption Spectroscopies for Streamlined Chemical Detection of Polycyclic Aromatic Hydrocarbon-Derived Compounds

  ACS Nano · 2023-12-08 · 33 citations

  article

  Polycyclic aromatic hydrocarbons (PAHs) constitute a class of universally prevalent carcinogenic environmental contaminants. It is increasingly recognized, however, that PAHs derivatized with oxygen, sulfur, or nitrogen functional groups are frequently more dangerous than their unfunctionalized counterparts. This much larger family of chemicals─polycyclic aromatic compounds─PACs─is far less well characterized than PAHs. Using surface-enhanced Raman and IR Absorption spectroscopies (SERS + SEIRA) combined on a single substrate, along with density functional theoretical (DFT) calculations, we show that direct chemical detection and identification of PACs at sub-parts-per-billion concentration can be achieved. Focusing our studies on 9,10-anthraquinone, 5,12-tetracenequinone, 9-nitroanthracene, and 1-nitropyrene as model PAC contaminants, detection is made possible by incorporating a hydroxy-functionalized self-assembled monolayer that facilitates hydrogen bonding between analytes and the SERS + SEIRA substrate. 5,12-Tetracenequinone was detected at 0.3 ppb, and the limit of detection was determined to be 0.1 ppb using SEIRA alone. This approach is straightforwardly extendable to other families of analytes and will ultimately facilitate fieldable chemical detection of these dangerous yet largely overlooked environmental contaminants.

  PublisherDOI

• Computational Chromatography: A Machine Learning Strategy for Demixing Individual Chemical Components in Complex Mixtures

  Zenodo (CERN European Organization for Nuclear Research) · 2022-12-06

  datasetOpen access

  This repository contains data for "Computational Chromatography: A Machine Learning Strategy for Demixing Individual Chemical Components in Complex Mixtures".

  PublisherDOI

Frequent coauthors

• Naomi J. Halas

  Rice University

  50 shared

• Peter Nordlander

  Rice University

  33 shared

• Surbhi Lal

  Rice University

  11 shared

• Dongmao Zhang

  Mississippi State University

  10 shared

• Pernilla Wittung‐Stafshede

  Chalmers University of Technology

  10 shared

• Felicia Tam

  Hartford Financial Services (United States)

  9 shared

• Ankit Patel

  Rice University

  9 shared

• Aoune Barhoumi

  7 shared

Labs

• Oara Neumann LabPI

Awards & honors

• Peter M and Ruth L Nicholas Postdoctoral Fellowship in Nanot…