Ganapathy Senthil Murugan

Fourier transform infrared (FTIR) spectroscopy provides label-free molecular fingerprints but often lacks sensitivity for trace analysis in aqueous media. Surface-enhanced infrared absorption (SEIRA) spectroscopy amplifies vibrational signals near nanostructured surfaces, yet conventional SEIRA platforms typically require sample drying, preconcentration, or noble metal substrates. Here, we introduce a monolithic all-silicon chip that integrates dielectric Mie-resonant micropillar arrays onto a microattenuated total reflection (µ-ATR) element using a complementary metal–oxide–semiconductor (CMOS)-compatible fabrication process. This µ-ATR SEIRA (µ-SEIRA) chip enables SEIRA directly in bulk aqueous media without sample drying, preconcentration, or noble metals. The µ-SEIRA chip provides spectrally tunable field enhancement, achieving a limit of detection (LOD) of 0.57 mM for aqueous sodium acetate, a 3.6-fold improvement over a flat silicon reference. Resonance tuning via the micropillar array geometry enables selective enhancement of specific vibrational bands, as demonstrated by the preferential enhancement of the Amide II band (∼1550 cm–1) over the Amide I band (∼1650 cm–1) in bovine serum albumin. The µ-SEIRA chip is low-cost, scalable, and disposable, reducing cross-contamination and throughput limitations associated with reusable ATR crystals for aqueous sample measurements.

Microstructured silicon attenuated total reflection (ATR) Fourier transform infrared (FTIR) elements, commonly known as microstructured silicon reflection elements (μSREs), are gaining popularity as a low-cost, disposable alternative to ATR crystals in fields such as medical diagnostics. They comprise a silicon chip, with light incident on microprisms on the underside guided into the chip and totally internally reflecting at the sample interface. However, such devices are prone to issues, including poor limit of detection compared to traditional ATR crystals, and fringes within their spectra, whose origin has not been conclusively identified in the literature. This work, verified by experimental data, mathematically reveals the origin of these fringes to be an acquired phase difference between beams incident on the same μSRE prism face upon input, but exiting the μSRE through separate prism faces, resulting in interference. This improved understanding of the behavior of light within the μSRE will enable researchers to select appropriate μSRE designs for their application, maximizing the potential of this technology.

Therapeutic drug monitoring (TDM) is the clinical practice of measuring specific drugs at designated intervals to maintain a constant concentration in a patient's bloodstream. However, detecting clinically relevant levels (typically in the μg/ml range) remains challenging outside the laboratory, as current analytical methods use elaborate sample preparation protocols and complicated measurement procedures. Here, we present a metasurface-assisted Fourier-transform infrared approach using dried samples that enhances molecular absorption by aligning their vibrational modes with the metasurface resonance; the metasurface supports a guided-mode resonance that hybridizes with the vibrational frequency of the drug molecule, thereby increasing the effective absorbance significantly. The metasurface topology also promotes ordered drug crystallization in regions with the strongest light–matter interaction, further contributing to the absorption enhancement. Using this design, we are able to consistently detect phenytoin in serum below the therapeutic range of 10–20 µg/ml by achieving a limit of detection of 1.4 μg/ml, which is limited by the drying process. This proof of concept for TDM at concentrations in the sub-clinical level highlights the significant potential for metasurface-enhanced spectroscopy and offers a path toward compact, high-performance infrared sensors for healthcare applications.

DNA length analysis is essential for genomic workflows including next-generation sequencing and fragmentomics-based diagnostics. Conventional approaches typically require large, expensive instrumentation and sample-destructive protocols with long processing times. Here we present a rapid, label-free approach integrating vibrational spectroscopy with deep learning to quantify DNA fragment length distributions. We demonstrate that ATR-FTIR and Raman spectroscopy capture length-dependent spectral features arising from phosphate backbone, nucleobase, and structural vibrations. Machine learning models trained on spectra acquired from purified monodisperse DNA (50–300 bp) predicted DNA length with high accuracy (R2 = 0.92–0.94), with multimodal fusion improving performance to R2 = 0.96. A convolutional neural network trained on DNA mixtures comprising molecules of different lengths also successfully recovered their fragment length profile. Transfer learning enabled adaptation to biological samples, achieving low prediction error (RMSE = 0.18–7.2%, Δμ = 12 bp). Importantly, the method requires only 4 μL of sample and 15 min of passive drying, with no consumables beyond cleaning materials, and allows full sample recovery. These results demonstrate the initial feasibility of vibrational spectroscopy as a rapid, label-free, and nondestructive approach for estimating DNA fragment length distributions.

Global quantification of DNA cytosine modifications, including 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC), is important for understanding cancer biology, though established methods require multistep workflows and costly instrumentation. Here, we show that attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy combined with regression modeling enables rapid, label-free, and nondestructive quantification of both modifications from DNA samples. Using Adenomatous Polyposis Coli (APC) promoter DNA standards spanning 0–100% modification, we identified modification-sensitive spectral features and observed that 5-hmC produces greater spectral changes than 5-mC. A univariate peak-ratio approach yielded strong linearity for both modifications (R2 = 0.97), while partial least-squares regression (PLSR) improved quantification accuracy to R2 = 0.99 (RMSE = 2.6%) for 5-hmC and R2 = 0.97 (RMSE = 5.7%) for 5-mC. In composite mixtures containing all three cytosine states, 5-hmC remained highly quantifiable (R2 = 0.97; RMSE = 5.1%), while 5-mC accuracy decreased (R2 = 0.90; RMSE = 9.6%), consistent with the greater spectral distinctiveness secondary to the hydroxymethyl group. Transferability was assessed using circulating tumor DNA (ctDNA), short cell-free DNA fragments shed from tumor cells into the bloodstream, comprising multiplexed reference material spanning seven genomic regions and a polydisperse fragment-length distribution (155–220 bp). After domain adaptation between synthetic and ctDNA spectra, we obtained a quantitative methylation calibration with R2 = 0.98 and RMSE = 5.2% under cross-validation. These results support ATR-FTIR spectroscopy as a viable platform for global cytosine modification quantification and establish proof-of-concept applicability to ctDNA analysis.

Global quantification of DNA cytosine modifications, including 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC), is important for understanding cancer biology, though established methods require multi-step workflows and costly instrumentation. Here we show that attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy combined with regression modelling enables rapid, label-free, and non-destructive quantification of both modifications from DNA samples. Using Adenomatous Polyposis Coli ( APC ) promoter DNA standards spanning 0-100% modification, we identified modification-sensitive spectral features and observed that 5-hmC produces greater spectral changes than 5-mC. A univariate peak-ratio approach yielded strong linearity for both modifications (R ² = 0.97), while partial least squares regression (PLSR) improved quantification accuracy to R ² = 0.99 (RMSE = 2.6%) for 5-hmC and R ² = 0.97 (RMSE = 5.7%) for 5-mC. In composite mixtures containing all three cytosine states, 5-hmC remained highly quantifiable (R ² = 0.97; RMSE = 5.1%), while 5-mC accuracy decreased (R ² = 0.90; RMSE = 9.6%), consistent with the greater spectral distinctiveness secondary to the hydroxymethyl group. Transferability was assessed using circulating tumour DNA (ctDNA), short cell-free DNA fragments shed from tumour cells into the bloodstream, comprising multiplexed reference material spanning seven genomic regions and a polydisperse fragment-length distribution (155-220 bp). After domain adaptation between synthetic and ctDNA spectra, we obtained a quantitative methylation calibration with R ² = 0.98 and RMSE = 5.2% under cross-validation. These results support ATR-FTIR spectroscopy as a viable platform for global cytosine modification quantification and establish proof-of-concept applicability to ctDNA analysis.

Nonlinear optical devices have attracted increasing interest, with lithium niobate being the benchmark material due to its strong second-order optical nonlinearity (SONL). However, its hardness, chemical inertness, and anisotropy present significant fabrication challenges, motivating the search for alternative platforms. Amorphous materials with poling-induced SONL offer promising opportunities, as their isotropic nature enables flexible optical design. In this work, we investigate amorphous sodium niobate thin films and demonstrate waveguide structures with active SONL through thermally controlled micrometer-scale poling combined with standard lithography. Using polarized second-harmonic generation (SHG) microscopy, we show precise spatial and geometrical control of the induced SONL, and its retention after waveguide processing steps, including UV lithography and dry-etching. The persistence of strong SHG signals, particularly near poling electrode edges, highlights the potential of aligning lithography mask with SONL patterns to enhance device performance, providing a foundation for developing efficient nonlinear photonic devices based on poled amorphous thin films.

Reducing measurement uncertainty is crucial to enable the adoption of rapid point-of-use techniques for clinical and industrial applications. Diagnosis of neonatal respiratory distress syndrome and liposome formulation quality control are two applications for which measuring the ratio of the lecithin to sphingomyelin composition of liposomes is important, for which no rapid measurement currently exists. Raman and infrared spectroscopies are two complementary approaches to examine characteristic molecular vibrations that can spectroscopically measure liposomes and, when combined with machine learning, predict their composition. We show that employing a data-fusion approach the uncertainty in the predicted compositions compared to the individual modalities (IR R²: 0.902 and Raman R²: 0.951) can be reduced to obtain more accurate and precise measurements (low-level fused model R²: 0.973, mean squared error: 0.024, prediction interval width: 0.303, high-level weighted fusion model R²: 0.970, mean squared error: 0.027, prediction interval width: 0.268).

Rapid point of care assessment of pulmonary surfactant composition by measuring the lecithin/sphingomyelin (L/S) ratio could improve management of patients with neonatal respiratory distress syndrome (nRDS). Attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) offers a practical route to making such measurements, but the influence of the sample solvent prior to drying on measurement repeatability is poorly understood. We compare films dried from dichloromethane (DCM) and water (AQ) solvents (DCM-dry route vs. AQ-dry route) by ATR-FTIR and show that spectra from the AQ-dry route increased the signal-to-noise ratio (SNR) of a representative (2920 cm⁻¹) absorption peak for the mixture from 20.13 to 128.20 and for human endotracheal aspirate (ETA) from 6.33 to 8.13. A mixed nested analysis of variance (ANOVA) showed that drying route accounted for 89.52% of mixture peak height variance and reduced percent relative standard deviation (%RSD) from 23.5% to 16.2%, corroborated by multivariate analysis for ETA. We further demonstrate that partial least squares regression (PLSR) models trained on AQ-dry mixture spectra predicted L/S (R² = 0.91; root mean square error (RMSE) = 0.31) with 95% prediction interval grey-zone interpretation around L/S = 2.2, complemented by a receiver operating characteristic area under the curve (ROC-AUC) of 0.978.