Single-Molecule Methane Sensing Using Palladium-Functionalized nIR Fluorescent Single-Walled Carbon Nanotubes

Scalable Photonic Nose Development through Corona Phase Molecular Recognition

Breath sensors promise early disease diagnosis through noninvasive, rapid analysis, but have struggled to reach clinical use due to challenges in scalability and multivariate data extraction. The current breath sensor design necessitates various channel materials and surface functionalization methods, which delays the process. Additionally, the limited options for channel materials that provide optimum sensitivity and selectivity further restrict the array size to a maximum of only 10 to 20 channels. To address these limitations, we propose a breath sensing array design process based on Corona Phase Molecular Recognition (CoPhMoRe), which enables the creation of an expansive library of nanoparticle interfaces and broad fingerprints for multiple analytes in the breath. Although CoPhMoRe has predominantly been utilized for liquid-phase sensing, its recent application to gas-phase sensing has shown significant potential for breath sensing. We introduce the recent demonstrations in the field and present the concept of a CoPhMoRe-based photonic-nose sensor array, leveraging fluorescent nanomaterials such as near-infrared single-walled carbon nanotubes. Additionally, we identified four critical milestones for translating CoPhMoRe into breath sensors for practical clinical applications.

Continuous Flow Chemistry and Bayesian Optimization for Polymer-Functionalized Carbon Nanotube-Based Chemiresistive Methane Sensors

We report the preparation of poly(ionic) polymer-wrapped single-walled carbon nanotube dispersions for chemiresistive methane (CH4) sensors with improved humidity tolerance. Single-walled CNTs (SWCNTs) were noncovalently functionalized by poly(4-vinylpyridine) (P4VP) with varied amounts of a poly(ethylene glycol) (PEG) moiety bearing a Br and terminal azide group (Br-R1). The quaternization of P4VP with Br-R1 was performed using continuous flow chemistry and Bayesian optimization-guided reaction selection. Polymers (PyBrR1) with different degrees of functionalization were used to disperse SWCNTs and subsequently incorporated into sensors containing a platinum complex as an aerobic oxidative catalyst with a polyoxometalate (POM) redox mediator to facilitate room-temperature CH4 sensing. As the degree of quaternization in the PyBrR1-CNT composites increased, improvements in response magnitude were observed, with nominally 10% quaternized PyBrR1 giving the largest response. Incorporation of PEG improved sensor stability at relative humidities between 57-90% versus sensors fabricated from CNT dispersions with unfunctionalized P4VP. Devices fabricated with these dispersions outperformed those prepared in situ under dry conditions, and exhibited greater stability at elevated humidities. The influence of Keggin-type POM character was also evaluated to identify alternative POMs for enhanced sensor performance at high humidity. In an effort to identify areas for further improvement in algorithm performance for polymer functionalization, a kinetically informed machine learning model was explored as a route to predict reactivity of pyridine units and alkyl bromides under flow conditions.

High Spatiotemporal Precision Mapping of Optical Nanosensor Array Using Machine Learning

Optical nanosensors, including single-walled carbon nanotubes (SWCNTs), provide real-time spatiotemporal reporting at the single-molecule level within a nanometer-scale area. However, their superior sensitivity also makes them susceptible to slight environmental influences such as reference analytes in media, external fluid flow, and mechanical modulations. Consequently, they often fail to achieve the optimal limit of detection (LOD) and frequently convey misinformation spatiotemporally. To address this challenge, we developed a single-pixel mapping technique for optical nanosensor arrays that operates with high spatiotemporal precision using machine learning. We systematically measured the spatial sensing images of various analyte concentrations below the LOD by using a near-infrared (nIR) fluorescent SWCNT nanosensor array. For dopamine (DA) as an example analyte, we extracted single-pixel level sensing features such as entropy, the Laplacian operator, and neighboring values under noise levels. We then trained the artificial intelligence (AI) model to accurately identify specific reaction pixels of the nanosensor array, even below the LOD region. Additionally, our method can distinguish subtle noise caused by fluid in the media or mechanical modulation of the array substrate. As a result, our approach significantly improved the detection sensitivity of the nanosensor array, achieving a 13-fold increase over the original LOD and halving the detection time of the reporter pixels, with F1 scores exceeding 0.9. This method not only lowers the LOD of optical nanosensors but also isolates sensor responses specific to the analyte, providing accurate spatiotemporal information to the user, even in noisy conditions. It can be universally applied to various optical nanosensor materials and analytes, maximizing the sensitivity and accuracy of the nanosensors used in diagnostics and analysis.

Artificial Intelligence-Enhanced Waveguide "Photonic Nose"- Augmented Sensing Platform for VOC Gases in Mid-Infrared

On-chip nanophotonic waveguide sensor is a promising solution for miniaturization and label-free detection of gas mixtures utilizing the absorption fingerprints in the mid-infrared (MIR) region. However, the quantitative detection and analysis of organic gas mixtures is still challenging and less reported due to the overlapping of the absorption spectrum. Here,an Artificial-Intelligence (AI) assisted waveguide "Photonic nose" is presented as an augmented sensing platform for gas mixture analysis in MIR. With the subwavelength grating cladding supported waveguide design and the help of machine learning algorithms, the MIR absorption spectrum of the binary organic gas mixture is distinguished from arbitrary mixing ratio and decomposed to the single-component spectra for concentration prediction. As a result, the classification of 93.57% for 19 mixing ratios is realized. In addition, the gas mixture spectrum decomposition and concentration prediction show an average root-mean-square error of 2.44 vol%. The work proves the potential for broader sensing and analytical capabilities of the MIR waveguide platform for multiple organic gas components toward MIR on-chip spectroscopy.

Monitoring Enzyme Activity Using Near-Infrared Fluorescent Single-Walled Carbon Nanotubes

Enzymes serve as pivotal biological catalysts that accelerate essential chemical reactions, thereby influencing a variety of physiological processes. Consequently, the monitoring of enzyme activity and inhibition not only yields crucial insights into health and disease conditions but also forms the basis of research in drug discovery, toxicology, and the understanding of disease mechanisms. In this context, near-infrared (NIR) fluorescent single-walled carbon nanotubes (SWCNTs) have emerged as effective tools for tracking enzyme activity and inhibition through diverse strategies. This perspective explores the physicochemical attributes of SWCNTs that render them well-suited for such monitoring. Additionally, we delve into the various strategies developed so far for successfully monitoring enzyme activity and inhibition, emphasizing the distinctive features of each principle. Furthermore, we contrast the benefits of SWCNT-based NIR probes with conventional gold standards in monitoring enzyme activity. Lastly, we highlight the current challenges faced in this field and suggest potential solutions to propel it forward. This perspective aims to contribute to the ongoing progress in biodiagnostics and seeks to engage the wider community in developing and applying enzymatic assays using SWCNTs.

Signal Amplification and Near-Infrared Translation of Enzymatic Reactions by Nanosensors

Enzymatic reactions are used to detect analytes in a range of biochemical methods. To measure the presence of an analyte, the enzyme is conjugated to a recognition unit and converts a substrate into a (colored) product that is detectable by visible (VIS) light. Thus, the lowest enzymatic turnover that can be detected sets a limit on sensitivity. Here, we report that substrates and products of horseradish peroxidase (HRP) and β-galactosidase change the near-infrared (NIR) fluorescence of (bio)polymer modified single-walled carbon nanotubes (SWCNTs). They translate a VIS signal into a beneficial NIR signal. Moreover, the affinity of the nanosensors leads to a higher effective local concentration of the reactants. This causes a non-linear sensor-based signal amplification and translation (SENSAT). We find signal enhancement up to ≈120x for the HRP substrate p-phenylenediamine (PPD), which means that reactions below the limit of detection in the VIS can be followed in the NIR (≈1000 nm). The approach is also applicable to other substrates such as 3,3'-5,5'-tetramethylbenzidine (TMB). An adsorption-based theoretical model fits the observed signals and corroborates the sensor-based enhancement mechanism. This approach can be used to amplify signals, translate them into the NIR and increase sensitivity of biochemical assays.