Energy Flow during the Plasmon Resonance-Driven Photocatalytic Reactions on Single Nanoparticles

Efficient and Accurate pH Determination with pH Test Strips Based on Machine Learning

The determination of pH values is crucial in various fields, such as analytical chemistry, medical diagnostics, and biochemical research. pH test strips, renowned for their convenience and cost-effectiveness, are commonly utilized for pH qualitative estimation. Recently, quantitative methods for determining pH values using pH test strips have been developed. However, these methods can be prone to errors due to environmental factors, such as lighting conditions, which affect the imaging quality of the pH test strips. To address these challenges, we developed an innovative approach that combines machine learning techniques with pH test strips for the quantitative determination of pH values. Our method involves extracting artificial features from the pH test strip images and combining them across multiple dimensions for comprehensive analysis. To ensure optimal feature selection, we developed a feature selection strategy based on SHAP importance. This strategy helps in identifying the most relevant features that contribute to accurate pH prediction. Furthermore, we integrated multiple machine learning algorithms, employing a robust stacking fusion strategy to establish a highly reliable pH value prediction model. Our proposed method automates the determination of pH values through pH test strips, effectively overcoming the limitations associated with environmental lighting interference. Experimental results demonstrate that this method is convenient, effective, and highly reliable for the determination of pH values.

Ultrasensitive detection of tyrosinase with click reaction-combined dark-field imaging platform

Tyrosinase (TYR) is an essential oxidase that is responsible for the regulation of multiple physiological processes and diseases. Achieving the trace and reliable detection of TYR in complex biological samples is of great significance for the diagnosis of TYR-related diseases, but which faces a great challenge. In this study, we developed an ingenious and powerful method for the ultrasensitive detection of TYR by click reaction-combined dark-field microscopy. This method begins with the formation of cuprous ions (Cu+) based on the reduction of copper ions (Cu2+) by ascorbic acid (AA). Subsequently, the formed Cu+ can catalyze the crosslinking between azide- and alkyne-functionalized gold nanoparticles, causing a significant red-shift in the scattering spectrum. However, AA can chelate with TYR, which inhibits the generation of Cu+ and subsequent click reaction, thus achieving TYR-controlled scattering spectral shift. The proposed sensing platform shows a good linear detection range of 0.01-0.8 U/L with a low detection limit of 0.003 U/L, which is three orders of magnitude lower than the best performance of TYR sensing probes reported to date. Most importantly, the strategy has the ability to reliably and accurately detect TYR in serum sample, suggesting its potential clinical application in diagnosing TYR-related diseases. This visual sensing platform offers promising prospects for future research in enzymatic analysis and biomedical diagnostics.

Enhancing Plasmonic Hot Electron Energy on Ag Surface by Amine Coordination

Plasmonic catalysis has emerged as a promising approach to solar-chemical energy conversion. Notably, hot carriers play a decisive role in plasmonic catalysis since only when their energy matches with the LUMO or HOMO energy of the reactant molecule, can the reaction be activated. However, the hot carrier energy depends on the intrinsic physicochemical properties of the plasmonic metal substrate and the interaction with incident light. Tuning the hot carrier energy is of great significance for plasmonic catalysis but remains challenging. Here, we demonstrate that the energy of hot electrons can be significantly elevated to an unprecedented level through the coordination of amines on Ag surface. The bonding of amines and Ag reduces the work function of nanoparticles, leading to the increase of hot electron energy by 0.4 eV. This enhancement of energy promotes the cleavage of C-X (X=Cl, F) bonds upon excitation by visible light. This study provides new insights for promoting plasmonic charge transfer and enhancing the photocatalytic performance of plasmon-mediated systems.

High Confidence Single Particle Analysis with Machine Learning

Single particle analysis can effectively determine the heterogeneity between particles based on the local information on a single particle, which is utilized extensively for monitoring chemical reactions and biological activities. However, the study of obtaining ensemble reaction information at the single particle level, which can obtain both the structural and functional heterogeneity of particles as well as the ensemble reaction information, is challenging because the selection of a single particle mainly depends on experience, which will lead to a certain randomness when analyzing the ensemble reaction with single particles. Using machine learning, it is demonstrated that the proposed intelligent single particle analysis strategy can provide single particle and ensemble analyses with high confidence. Convolutional neural network and Gaussian mixture model were utilized to develop a machine learning model for resonance scattering imaging analysis of plasmonic nanoparticles. It can identify the scattered light of single particles and select representative or diverse particles. When single particle scattering imaging is used to obtain ensemble information on the reaction, the error caused by the selection of individual particles can be significantly reduced by selecting representative particles. In addition, the real situation of the reaction can be better revealed by selecting diverse particles. These results indicate that the intelligent single particle analysis strategy has great potential for imaging analysis and biological sensing.

Active Site Engineering on Plasmonic Nanostructures for Efficient Photocatalysis

Plasmonic nanostructures have shown immense potential in photocatalysis because of their distinct photochemical properties associated with tunable photoresponses and strong light-matter interactions. The introduction of highly active sites is essential to fully exploit the potential of plasmonic nanostructures in photocatalysis, considering the inferior intrinsic activities of typical plasmonic metals. This review focuses on active site-engineered plasmonic nanostructures with enhanced photocatalytic performance, wherein the active sites are classified into four types (i.e., metallic sites, defect sites, ligand-grafted sites, and interface sites). The synergy between active sites and plasmonic nanostructures in photocatalysis is discussed in detail after briefly introducing the material synthesis and characterization methods. Active sites can promote the coupling of solar energy harvested by plasmonic metal to catalytic reactions in the form of local electromagnetic fields, hot carriers, and photothermal heating. Moreover, efficient energy coupling potentially regulates the reaction pathway by facilitating the excited state formation of reactants, changing the status of active sites, and creating additional active sites using photoexcited plasmonic metals. Afterward, the application of active site-engineered plasmonic nanostructures in emerging photocatalytic reactions is summarized. Finally, a summary and perspective of the existing challenges and future opportunities are presented. This review aims to deliver some insights into plasmonic photocatalysis from the perspective of active sites, expediting the discovery of high-performance plasmonic photocatalysts.

Single-Particle Measurements of Nanocatalysis with Dark-Field Microscopy

Due to the complexity of heterogeneous reactions and heterogeneities of individual catalyst particles in size, morphology, and the surrounding medium, it is very important to characterize the structure of nanocatalysts and measure the reaction process of nanocatalysis at the single-particle level. Traditional ensemble measurements, however, only provide averaged results of billions of nanoparticles (NPs), which do not help reveal structure–activity relationships and may overlook a few NPs with high activity. The advent of dark-field microscopy (DFM) combined with plasmonic resonance Rayleigh scattering (PRRS) spectroscopy provides a powerful means for directly recording the localized surface plasmon resonance (LSPR) spectrum of single plasmonic nanoparticles (PNPs), which also enables quantitative measurements. In recent years, DFM has developed rapidly for a series of single-particle catalytic reactions such as redox reactions, electrocatalytic reactions, and DNAzyme catalysis, with the ability to monitor the catalytic reaction process in real time and reveal the catalytic mechanism. This review provides a comprehensive overview of the fundamental principles and practical applications of DFM in measuring various kinds of catalysis (including chemocatalysis, electrocatalysis, photocatalysis, and biocatalysis) at the single-particle level. Perspectives on the remaining challenges and future trends in this field are also proposed.