Ultrasensitive NO Sensor Based on a Nickel Single-Atom Electrocatalyst for Preliminary Screening of COVID-19

Waveguide-Based Microwave Nitric Oxide Sensor for COVID-19 Screening: Mass Transfer Modulation Effect on Hollow Confined WO3 Structures

Serious acute respiratory syndrome coronavirus-2 (SARS-CoV-2) poses a tremendous threat to global public health. Recently, the Food and Drug Administration approved the emergency use of volatile organic components as detection biomarkers for COVID-19, ushering in a new era of portable, simple, and rapid epidemiological screening based on breath diagnosis. Nitric oxide (NO) is an important biomarker indicating the degree of inflammation in the respiratory tract. In this study, a hollow multishelled structured WO3 (HoMSs-WO3)-based waveguide microwave gas sensor (MGS) was fabricated to detect trace levels of NO in exhaled breath for the preliminary diagnosis of COVID-19. The sensor showed excellent reusability and selectivity and efficiently detected NO in the 10-100 ppb, with a sensitivity of 39.27 dB/ppm and a detection limit of 2.52 ppb. In addition, a sound correlation was observed in the measurement results between the MGS and the Sunvou detector for detecting NO from the exhaled breath of clinical COVID-19 patients. The difference between the two measurements was within 1.96 standard bias, and the consistency range was -12 to 17 ppb, thus fully demonstrating the significant potential of the sensor in COVID-19 screening.

Materials Containing Single-, Di-, Tri-, and Multi-Metal Atoms Bonded to C, N, S, P, B, and O Species as Advanced Catalysts for Energy, Sensor, and Biomedical Applications

Modifying the coordination or local environments of single-, di-, tri-, and multi-metal atom (SMA/DMA/TMA/MMA)-based materials is one of the best strategies for increasing the catalytic activities, selectivity, and long-term durability of these materials. Advanced sheet materials supported by metal atom-based materials have become a critical topic in the fields of renewable energy conversion systems, storage devices, sensors, and biomedicine owing to the maximum atom utilization efficiency, precisely located metal centers, specific electron configurations, unique reactivity, and precise chemical tunability. Several sheet materials offer excellent support for metal atom-based materials and are attractive for applications in energy, sensors, and medical research, such as in oxygen reduction, oxygen production, hydrogen generation, fuel production, selective chemical detection, and enzymatic reactions. The strong metal-metal and metal-carbon with metal-heteroatom (i.e., N, S, P, B, and O) bonds stabilize and optimize the electronic structures of the metal atoms due to strong interfacial interactions, yielding excellent catalytic activities. These materials provide excellent models for understanding the fundamental problems with multistep chemical reactions. This review summarizes the substrate structure-activity relationship of metal atom-based materials with different active sites based on experimental and theoretical data. Additionally, the new synthesis procedures, physicochemical characterizations, and energy and biomedical applications are discussed. Finally, the remaining challenges in developing efficient SMA/DMA/TMA/MMA-based materials are presented.

Platinum single atom on CsPbBr3 nanocrystals as electrocatalyst boosts electrochemical sensing of ascorbic acid

Monitoring ascorbic acid (AA) levels in human body can provide valuable clues for disease diagnosis. Anchoring noble metal single atoms on perovskite substrate is a promising strategy to design electrocatalysts with outstanding electrocatalytic performance. Herein, we design an electrochemical method for detecting AA by utilizing Pt single atoms-doped CsPbBr3 nanocrystals (Pt SA/CsPbBr3 NCs) fixed on a glassy carbon electrode as an electrochemical catalyst. The uncharged 3,5,3',5'-tetramethylbenzidine (TMB) undergoes oxidation to form the positively charged oxidized TMB (oxTMB) owing to the exceptional electrochemical catalytic performance of Pt SA/CsPbBr3 NCs. Subsequently, the target AA reduces oxTMB to TMB, which is then electrocatalytically oxidized to oxTMB, producing significant oxidation current. In this way, such characteristic provides a sensitive electrochemical strategy for AA detection, achieving a concentration range of 50-fold with the detection limit of 0.0369 μM. The developed electrochemical method also successfully generates accurate detection response of AA in complex sample media (urine). Overall, this approach is expected to offer a novel way for early disease diagnosis.

Electrochemical Detection of Gasotransmitters: Status and Roadmap

Gasotransmitters, including nitric oxide (NO), carbon monoxide (CO), and hydrogen sulfide (H2S), are a class of gaseous, endogenous signaling molecules that interact with one another in the regulation of critical cardiovascular, immune, and neurological processes. The development of analytical sensing mechanisms for gasotransmitters, especially multianalyte mechanisms, holds vast importance and constitutes a growing area of study. This review provides an overview of electrochemical sensing mechanisms with an emphasis on opportunities in multianalyte sensing. Electrochemical methods demonstrate good sensitivity, adequate selectivity, and the most well-developed potential for the multianalyte detection of gasotransmitters. Future research will likely address challenges with sensor stability and biocompatibility (i.e., sensor lifetime and cytotoxicity), sensor miniaturization, and multianalyte detection in biological settings.

Ultrasensitive Online NO Sensor Based on a Distributed Parallel Self-Regulating Neural Network and Ultraviolet Differential Optical Absorption Spectroscopy for Exhaled Breath Diagnosis

The concentration of fractional exhaled nitric oxide (FeNO) is closely related to human respiratory inflammation, and the detection of its concentration plays a key role in aiding diagnosing inflammatory airway diseases. In this paper, we report a gas sensor system based on a distributed parallel self-regulating neural network (DPSRNN) model combined with ultraviolet differential optical absorption spectroscopy for detecting ppb-level FeNO concentrations. The noise signals in the spectrum are eliminated by discrete wavelet transform. The DPSRNN model is then built based on the separated multipeak characteristic absorption structure of the UV absorption spectrum of NO. Furthermore, a distributed parallel network structure is built based on each absorption feature region, which is given self-regulating weights and finally trained by a unified model structure. The final self-regulating weights obtained by the model indicate that each absorption feature region contributes a different weight to the concentration prediction. Compared with the regular convolutional neural network model structure, the proposed model has better performance by considering the effect of separated characteristic absorptions in the spectrum on the concentration and breaking the habit of bringing the spectrum as a whole into the model training in previous related studies. Lab-based results show that the sensor system can stably achieve high-precision detection of NO (2.59-750.66 ppb) with a mean absolute error of 0.17 ppb and a measurement accuracy of 0.84%, which is the best result to date. More interestingly, the proposed sensor system is capable of achieving high-precision online detection of FeNO, as confirmed by the exhaled breath analysis.

Theoretical characterization of two-photon fluorescent probes for nitric oxide detection: sensing mechanism, photophysical properties and protonation effects

Nitric oxide (NO) is an important signal molecule in biological systems and is correlated with many physiological processes and pathological diseases. To date, numerous fluorescent probes based on o-diamino aromatics have been designed and synthesized for NO detection utilizing the principle of photoinduced electron transfer (PET). However, the underlying PET mechanism has rarely been validated, and a systematic computational study on the photophysical properties is urgently desired. In this study, we used a theoretical protocol to comparatively investigate the sensing mechanism, photophysical properties and protonation effects of two emblematic probes NINO and PYSNO in aqueous solution, which combines a polarizable continuum model (PCM), time-dependent density functional theory (TD-DFT) and thermal vibration correlation function formalism (TVCF). Our findings reveal that the weak emission of NINO is due to activated PET with negative driving energy and blocked fluorescence with significant charge separation. In contrast, the poor luminescence of PYSNO is caused by the facilitated non-radiative dissipation, even though the fluorescence emission remains unobstructed. Although NINO has been successfully used in two-photon microscopy for detecting NO, we suggest that PYSNO possesses a superior two-photon absorption (TPA) cross section in the near-infrared region. The protonation effects suggest that both probes can function effectively in practical acidic lysosomal environments. Our study opens a new avenue for understanding the mechanism and predicting the properties of two-photon fluorescent probes for NO detection, thus aiding the rational design of efficient fluorescent sensors.

Rh single-atom nanozymes for efficient ascorbic acid oxidation and detection

The management of ascorbic acid (AA) in biological fluids is of significant importance for body functions and human health, yet challenging due to the lack of high-performance sensing catalysts. Herein, we report the design of Rh single-atom nanozymes (Rh SAzymes) by mimicking the active sites of ascorbate peroxidase toward efficient electrocatalytic oxidation and detection of AA. Benefiting from the enzyme-mimicking single-atom coordination, the Rh SAzyme exhibits an unprecedented electrocatalytic activity for AA oxidation with an onset potential as low as 0.02 V (vs. Ag/AgCl). Combined with the screen-printing technology, a miniaturized Rh SAzyme biosensor was firstly constructed for tracking dynamic trends of AA in the human subject and detecting AA content in nutritional products. The as-prepared biosensor exhibits excellent detection performances with a wide linear range of 10.0 μM-53.1 mM, a low detection limit of 0.26 μM, and a long stability of 28 days. This work opens a door for the design of artificial single-atom electrocatalysts to mimic natural enzymes and their subsequent application in biosensors.