Rational Design of 3D Polymer Corona Interfaces of Single-Walled Carbon Nanotubes for Receptor-Free Virus Recognition.

Facing the escalating threat of viruses worldwide, the development of efficient sensor elements for rapid virus detection has never been more critical. Traditional point-of-care (POC) sensors struggle due to their reliance on fragile biological receptors and limited adaptability to viral strains. In this study, we introduce a nanosensor design for receptor-free virus recognitions using near-infrared (NIR) fluorescent single-walled carbon nanotubes (SWCNTs) functionalized with a poly(ethylene glycol) (PEG)-phospholipid (PEG-lipid) array. Three-dimensional (3D) corona interfaces of the nanosensor array enable selective and sensitive detection of diverse viruses, including Ebola, Lassa, H3N2, H1N1, Middle East respiratory syndrome (MERS), severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1), and SARS-CoV-2, even without any biological receptors. The PEG-lipid components, designed considering chain length, fatty acid saturation, molecular weight, and end-group moieties, allow for precise quantification of viral recognition abilities. High-throughput automated screening of the array demonstrates how the physicochemical properties of the PEG-lipid/SWCNT 3D corona interfaces correlate with viral detection efficiency. Utilizing molecular dynamics and AutoDock simulations, we investigated the impact of PEG-lipid components on 3D corona interface formation, such as surface coverage and hydrodynamic radius and specific molecular interactions based on chemical potentials. Our findings not only enhance detection specificity across various antigens but also accelerate the development of sensor materials for promptly identifying and responding to emerging antigen threats.

Nanosensor Chemical Cytometry: Advances and Opportunities in Cellular Therapy and Precision Medicine.

With the definition of therapeutics now encompassing transplanted or engineered cells and their molecular products, there is a growing scientific necessity for analytics to understand this new category of drugs. This Perspective highlights the recent development of new measurement science on label-free single cell analysis, nanosensor chemical cytometry (NCC), and their potential for cellular therapeutics and precision medicine. NCC is based on microfluidics integrated with fluorescent nanosensor arrays utilizing the optical lensing effect of a single cell to real-time extract molecular properties and correlate them with physical attributes of single cells. This new class of cytometry can quantify the heterogeneity of the multivariate physicochemical attributes of the cell populations in a completely label-free and nondestructive way and, thus, suggest the vein-to-vein conditions for the safe therapeutic applications. After the introduction of the NCC technology, we suggest the technological development roadmap for the maturation of the new field: from the sensor/chip design perspective to the system/software development level based on hardware automation and deep learning data analytics. The advancement of this new single cell sensing technology is anticipated to aid rich and multivariate single cell data setting and support safe and reliable cellular therapeutics. This new measurement science can lead to data-driven personalized precision medicine.

Physicochemical Profiling of Macrophage Heterogeneity Using Deep Learning Integrated Nanosensor Cytometry.

Label-free single-cell analytics have been developed for understanding the collective immune response mechanism of immune cells. However, it remains difficult to analyze the physicochemical properties of a single cell in high spatiotemporal resolution for an immune cell having dynamic morphological changes and significant molecular heterogeneities. It is deemed due to the absence of a sensitive molecular sensing construct and single-cell imaging analytic program. In this study, we developed a deep learning integrated nanosensor chemical cytometry (DI-NCC) platform, which combines a fluorescent nanosensor array in microfluidics and a deep learning model for cell feature analysis. The DI-NCC platform possesses the capability to collect rich, multivariate data sets for each individual immune cell (e.g., macrophage) within the population. We obtained LPS+ (n = 25) and LPS- (n = 61) near-infrared images and analyzed 250 cells/mm2 in 1 µm spatial resolution and 0 to 1.0 confidence level even with overlapped or adhered cell configurations. This enables automatic quantification of the activation and nonactivation levels of a single macrophage upon instantaneous immune stimulations. Furthermore, we support the activation level quantified by deep learning with heterogeneities analysis of both biophysical (cell size) and biochemical (nitric oxide efflux) properties. The DI-NCC platform can be promising for activation profiling of dynamic heterogeneity variations of cell populations.

Rapid and Reversible Sensing Performance of Hydrogen-Substituted Graphdiyne.

The design of new nanomaterials for rapid and reversible detection of molecules in existence is critical for real-world sensing applications. Current nanomaterial libraries such as carbon nanotubes, graphene, MoS2, and MXene are fundamentally limited by their slow detection speed and small signals; thus, the atomic-level material design of molecular transport pathways and active binding sites must be accompanied. Herein, we fully explore the chemical and physical properties of a hydrogen-substituted graphdiyne (HsGDY) for its molecular sensing properties. This new carbon framework comprises reactive sp carbons in acetylenic linkages throughout the 16.3 Å nanopores and allows for detecting target molecules (e.g., H2) with an exceptionally high sensitivity (ΔR/Rb = 542%) and fast response/recovery time (τ90 = 8 s and τ10 = 38 s) even without any postmodification process. It possesses 2 orders of magnitude higher sensing ability than that of existing nanomaterial libraries. We demonstrate that rapid and reversible molecular binding is attributed to the cooperative interaction with adjacent double sp carbon in the layered nanoporous structure of HsGDY. This new class of carbon framework provides fundamental solutions for nanomaterials in reliable sensor applications that accelerate real-world interfacing.

Top-Down Approaches for 10 nm-Scale Nanochannel: Toward Exceptional H2S Detection.

Metal oxide semiconductors (MOS) have proven to be most powerful sensing materials to detect hydrogen sulfide (H2S), achieving part per billion (ppb) level sensitivity and selectivity. However, there has not been a way of extending this approach to the top-down H2S sensor fabrication process, completely limiting their commercial-level productions. In this study, we developed a top-down lithographic process of a 10 nm-scale SnO2 nanochannel for H2S sensor production. Due to high-resolution (15 nm thickness) and high aspect ratio (>20) structures, the nanochannel exhibited highly sensitive H2S detection performances (Ra/Rg = 116.62, τres = 31 s at 0.5 ppm) with selectivity (RH2S/Racetone = 23 against 5 ppm acetone). In addition, we demonstrated that the nanochannel could be efficiently sensitized with the p-n heterojunction without any postmodification or an additional process during one-step lithography. As an example, we demonstrated that the H2S sensor performance can be drastically enhanced with the NiO nanoheterojunction (Ra/Rg = 166.2, τres = 21 s at 0.5 ppm), showing the highest range of sensitivity demonstrated to date for state-of-the-art H2S sensors. These results in total constitute a high-throughput fabrication platform to commercialize the H2S sensor that can accelerate the development time and interface in real-life situations.