Research

One photon in a billion

Raman scattering is a rare event. Illuminate a molecule and almost every photon bounces off with the energy it arrived with; roughly one in a billion leaves having traded a quantum of energy with a vibrating bond. That tiny fraction carries a fingerprint specific enough to name the molecule. Everything below is about making that fingerprint loud enough, clean enough and repeatable enough to trust.

The physics

Why the signal needs help

An incident photon at frequency ν0 can leave a molecule in a higher vibrational state, emerging red-shifted by exactly the vibrational frequency of the bond that absorbed the difference. The shift, not the absolute wavelength, is the observable — which is why a Raman spectrum is plotted against wavenumber in cm⁻¹ and looks the same whichever laser you excite it with.

The problem is the cross-section. Spontaneous Raman scattering is about twelve orders of magnitude weaker than fluorescence, so a dilute biological sample produces a signal that sits underneath its own background. Two things rescue it. First, a plasmonic surface: conduction electrons in a gold or silver nanostructure oscillate collectively with the incident field and concentrate it into gaps a few nanometres across. Because the enhancement scales roughly as the fourth power of the local field, a molecule sitting in one of those hot spots can scatter many orders of magnitude more strongly than the same molecule in free solution. Second, analysis: with the right preprocessing and classification, information spread across a whole spectrum can separate classes that no single band resolves.

Both rescues have a cost. Enhancement that varies wildly across a substrate turns quantitative measurement into guesswork, and a classifier trained on inconsistently preprocessed spectra will happily learn the preprocessing. Much of the work is in controlling those two failure modes.

Current work

Active directions

01 · Substrates

Reproducible plasmonic hot spots

Designing and characterising nanostructured surfaces where the enhancement factor is uniform enough to compare one measurement against another. Reproducibility, not peak enhancement, is what decides whether a SERS assay can be quantitative.

Gold nanostructuresEnhancement factorUniformity
02 · Diagnostics

Receptor-free respiratory virus detection

A machine-learning-integrated SERS platform that identifies respiratory viruses from their intrinsic vibrational fingerprint rather than through an antibody or aptamer. Removing the recognition element removes the component that usually sets the cost, the shelf life and the development time of a new assay.

Label-freeRapid diagnosticsVirology
03 · Analysis

Classification that survives a new batch

Building preprocessing and classification pipelines whose performance holds when the substrate batch, the laser power or the operator changes — the point at which most published spectral classifiers quietly stop working.

ChemometricsValidationGeneralisation
04 · Instruments

Fibre and integrated photonic sensing

Fibre Bragg gratings and guided-wave elements as a route from bench measurement toward a deployable instrument, and as sensing modalities in their own right for strain, temperature and refractive index.

FBGWaveguidesField deployment

Methods and instrumentation

What I actually do in a week

Spectroscopy
Raman · SERS · FTIR · UV-Vis · fluorescence
Nanomaterials
Colloidal and lithographic plasmonic substrates
Characterisation
SEM · AFM · XRD · DLS
Analysis
Python · MATLAB · scikit-learn · custom JS
Photonics
Fibre optics · FBG · waveguide coupling
Writing
LaTeX · figure preparation · reproducible pipelines

The analysis half of that list is where RNSpectraX came from — it began as a set of scripts for my own spectra and turned into something a collaborator could open without installing anything. Its companion, RNBeamX, does the same for the other half of every figure: the optical setup, with the beams traced for real.

Cite as: Naik, R., & Srivastava, S. K. (2026). RNSpectraX (Spectral Plotter and Analytics Suite) (Version 3.4) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.23593680 · Naik, R., & Srivastava, S. K. (2026). RNBeamX (Optical Setup Designer) (Version 1.2) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.23697703