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The research programs in our laboratory combine chemistry, nanotechnology, machine-learning and materials science approaches to develop functional nanostructures with novel catalysis, plasmonic and sensing applications. Our research activities involve nanoparticle synthesis, surface chemistry, self-assembly, nanopatterning, nanofabrication, and materials and device characterization.
Molecular sensing is key for diverse applications including medical diagnostics, environmental monitoring, and food analysis. To achieve enhanced SERS detection performances, we leverage a range of chemical interactions, such as covalent bonding, hydrogen bonding and electrostatic interactions, between target analyte molecules and various receptor molecules grafted on our nanostructures. This approach offers two key benefits: (1) by bringing the analytes close to the plasmonic surface, we can increase the effective concentrations and generate higher signal responses, and (2) by generating differential interaction profiles, we can trigger analyte-specific spectral fingerprint and accurately identify analytes. In addition, to amplify spectral differences for highly similar analytes or complex, multicomponent mixtures, we have developed ‘SERS super-profiles’ which are horizontal combinations of the SERS fingerprint of multiple receptors. Our results demonstrate the effectiveness of this approach across a wide range of analytes, including toxic, polluting gases such as NO2 and SO2, flavor molecules such as menthol and limonene, as well as more complex targets such as microorganisms and breath metabolites in COVID-19 breath. Such platforms exhibit huge potential as next generation molecular nanosensors.
In-depth analysis of the inherently complex SERS spectral data often holds the key to elucidating the wealth of chemical information that is embedded within. Our aim is to realize the integration of machine learning algorithms for SERS based analytics in four different ways – (1) As dimensionality reduction techniques to condense complex spectral variations into principal changes, (2) As evaluators to determine which input spectral features are the most important for classification or regression, (3) As predictive modelling methods to construct accurate classification or regression models and (4) As a robust approach to guide downstream feature extraction and engineering in an ensemble analytical framework involving multiple processes or models. Our results illustrate the promising potential to translate SERS-based nanosensors for practical use in biomedical, environmental and food industries.
Nanostructures have attracted huge interest as a rapidly growing class of materials for many applications ranging from biomedical, sensing and catalysis. The characterization of nanoparticles’ morphological properties such as shape, size, and surface characteristics is important as they dictate the particles’ properties and thus applications. The most widely used techniques such electron microscopy and x-ray diffractions are time-consuming, expensive and ill-suited for commercial use. Our group aims to leverage feature engineering and machine learning in combination with high-throughput analytical techniques for rapid, reliable and reproducible characterization nanoparticles’ morphological properties. In addition, our ML-driven strategy permits a data-driven approach to investigate the structure-property relationship of nanoparticles which is the cornerstone of nanomaterial research.
Responding to the global demand for faster and less invasive COVID-19 test, our group partnered with National Center for Infectious Disease to develop a SERS-based breathalyzer, named “TracieX” for COVID-19 detection. The TracieX breathalyzer can identify COVID-19 patients using a single breath, boasting sensitivity and specificity of >95%. TracieX is non-invasive in nature and avoids the unpleasant experience of a nose or throat swab. It can provide a test result within 2 minutes after the breath test is taken, making it a fast and effective screening tool for large events and places with high traffic flow.
We design “plasmonic nose” by integrating a functional zeolitic imidazolate framework (ZIF) coating over an array of plasmonic Ag nanocubes (Ag@ZIF) to “sniff out” gas/VOC vapors from air at molecular-level accuracy with detection sensitivity far superior than a human nose. Our plasmonic nose uniquely employ multifaceted strategies to detect gaseous molecules at trace level– (1) using ZIF to continuously accumulate gaseous molecules into a pseudo high-pressure microenvironment directly over plasmonic surfaces for efficient molecular read-out (as affirmed in our previous mechanistic investigations), and (2) intensifying electromagnetic hotspots by manipulating plasmonic coupling between adjacent Ag nanocubes. Using toxic VOC sensing as a proof-of-concept demonstration, our plasmonic nose enables the in-situ investigations into gas adsorption kinetics and quantitative detection of non-adsorbing toluene vapor from 200 - 20000 ppm. The SERS fingerprint also permits molecular-level recognition of various VOCs (e.g. chloroform and 2-naphthalenethiol), effectively eliminating false positives typical in commercial gas sensor. Moving beyond toxic gas/VOC sensing, our plasmonic nose also potentially offers tremendous opportunities to investigate air-to-fuel conversion, air remediation as well as breath-based biodiagnosis.
We demonstrate plasmonic liquid marble as a substrate-less analytical platform. When coupled with ultrasensitive SERS, it is capable of quantitative examination of multiple analyte(s) at sub-microliter volume. Simultaneous two-phase analyte detection at the interfacial of aqueous and organic solvents can also be achieved using our marble. The detection limit is 0.3 fmol, corresponding to an analytical enhancement factor of 5×10^8. The plasmonic liquid marbles is important for applications in lab-on-a-chip systems for on-site ultratrace and/or quantitative sensing.
The self-assembly of nanoparticles into superlattices is an important bridge between nanotechnology and macroscopic real-world applications. One of the grand challenges in nanoparticle self-assembly is to achieve superlattice diversity, enabling on-demand fabrication of materials for targeted applications. Our group aims to make use of nanoscale surface chemistry to direct the self-assembly of various anisotropic nanoparticles into diverse superlattices. We investigate the fundamental driving forces behind the formation of the observed superlattices, in the process answering the scientific question of ‘how does self-assembly occur?’ In addition, we also explore the applications of the assembled superlattices in various fields, such as (bio)chemical sensing and toxin detection.
