Observing atomic layer electrodeposition on single nanocrystals surface by dark field spectroscopy

How local electric field regulates C-C coupling at a single nanocavity in electrocatalytic CO2 reduction

C-C coupling is of utmost importance in the electrocatalytic reduction of CO2, as it governs the selectivity of diverse product formation. Nevertheless, the difficulties to directly observe C-C coupling pathways at a specific nanocavity hinder the advances in catalysts and electrolyzer design for efficient high-value hydrocarbon production. Here we develop a nano-confined Raman technology to elucidate the influence of the local electric field on the evolution of C-C coupling intermediates. Through precise adjustments to the Debye length in nanocavities of a copper catalyst, the overlapping of electrical double layers drives a transition in the C-C coupling pathway at a specific nanocavity from *CHO-*CO coupling to the direct dimerization of *CO species. Experimental evidence and simulations validate that a reduced potential drop across the compact layer promotes a higher yield of CO and promotes the direct dimerization of *CO species. Our findings provide insights for the development of highly selective catalyst materials tailored to promote specific products.

High-Throughput Identification of Single Nanoparticles via Electrochemically Assisted High-Resolution Plasmonic Scattering Interferometric Microscopy

The identification of nanoparticles within heterogeneous mixtures poses significant challenges due to the similarity in physical properties among different nanomaterials. Here, we present electrochemically assisted high-resolution plasmonic scattering interferometric microscopy (HR-PSIM). This technique allows for the high-throughput identification of nanoparticles by accurately measuring the refractive index of individual nanoparticles without interference from background signals. Through elimination of parabolic scattering interference and employing electrochemical modulation, HR-PSIM demonstrates high spatial resolution and stability against background noise, enabling the differentiation of nanoparticles with closely matched refractive indices, such as Au and Ag nanoparticles. The efficacy of this method is demonstrated through its application in real-time, label-free imaging of nanoparticle electrochemical activity, providing a platform for the precise and high-throughput characterization of nanomaterials. The robustness of our approach against electrochemical interference and its high spatial resolution mark a significant advancement in the field of nanomaterial analysis, promising wide-ranging applications in nanoparticle research and beyond.

Ultrasensitive Plasmon-Enhanced Infrared Spectroelectrochemistry

IR spectroelectrochemistry (EC-IR) is a cutting-edge operando method for exploring electrochemical reaction mechanisms. However, detection of interfacial molecules is challenged by the limited sensitivity of existing EC-IR platforms due to the lack of high-enhancement substrates. Here, we propose an innovative plasmon-enhanced infrared spectroelectrochemistry (EC-PEIRS) platform to overcome this sensitivity limitation. Plasmonic antennae with ultrahigh IR signal enhancement are electrically connected via monolayer graphene while preserving optical path integrity, serving as both the electrode and IR substrate. The [Fe(CN)6 ]3- /[Fe(CN)6 ]4- redox reaction and electrochemical CO2 reduction reaction (CO2 RR) are investigated on the EC-PEIRS platform with a remarkable signal enhancement. Notably, the enhanced IR signals enable a reconstruction of the electrochemical curve of the redox reactions and unveil the CO2 RR mechanism. This study presents a promising technique for boosting the in-depth understanding of interfacial events across diverse applications.

Optically Imaging In Situ Effects of Electrochemical Cycling on Single Nanoparticle Electrocatalysis

Single-nanoparticle studies often need one or a series of nanoparticle populations that are designed with differences in a nominally particular structural parameter to clarify the structure-activity relationship (SAR). However, the heterogeneity of various properties within any population would make it rather difficult to approach an ideal one-parameter control. In situ modification ensures the same nanoparticle to be investigated and also avoids complicating effects from the otherwise often needed ex situ operations. Herein, we apply electrochemical cycling to single platinum nanoparticles and optically examine their SAR. An electrocatalytic fluorescent microscopic method is established to evaluate the apparent catalytic activity of a number of single nanoparticles toward the oxygen reduction reaction. Meanwhile, dark-field microscopy with the substrate electrode under a cyclic potential control is found to be able to assess the electrochemically active surface area (ECSA) of single nanoparticles via induced chloride redox electrochemistry. Consequently, nanoparticles with drastically increased catalytic activity are discovered to have larger ECSAs upon potential regulation, and interestingly, there are also a few particles with decreased activity, as opposed to the overall trend, that all develop a smaller ECSA in the process. The deactivated nanoparticles against the overall enhancement effects of potential cycling are revealed for the first time. As such, the SAR of single nanoparticles when subjected to an in situ structural control is optically demonstrated.

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.

Shaping Copper Oxide Layers on Gold Nanoparticle Ensembles by Controlled Electrodeposition with Single Particle Scatterometry

Electrodeposition of copper on gold nanoelectrode ensembles result in the formation of uniform copper oxide layers on individual nanoparticles. A linear sweep of voltammetric change induces three distinct morphologies dependent upon particle density. Ex situ imaging and in situ scatterometry at a single-particle level identifies multi-step electrochemical growth sequences that deviated from classical nucleation and growth pathways. In addition, the study demonstrated the possibility of synthesizing sophisticated structures based on the symmetry of nanoelectrodes. This result guides the nanoscale morphology control of electrode ensembles with potential application in electrocatalysis and sensing.

Synthesis of Controllable Cu Shells on Au Nanoparticles with Electrodeposition: A Systematic in Situ Single Particle Study

Bimetallic Cu on Au nanoparticles with controllable morphology and optical properties were obtained via electrochemical synthesis. In particular, multilobed structures with good homogeneity were achieved through the optimization of experimental parameters such as deposition current, charge transfer, and metal ion concentration. A hyperspectral dark field scattering setup was used to characterize the electrodeposition on a single particle level, with changes in localized surface plasmon resonance frequency correlated with deposition charge transfer and amount of Cu deposited as determined by electron microscopy. This demonstrated the ability to tune morphology and spectra through electrochemical parameters alone. Time-resolved in situ measurements of single particle spectra were obtained, giving an insight into the kinetics of the deposition process. Nucleation of multiple cubes of Cu initially occurs preferentially on the tips of Au nanoparticles, before growing and coalescing to form a multilobed, lumpy shell. Modifying the surface of Au nanoparticles by plasma treatment resulted in thicker and more uniform Cu shells.

Facile Synthesis of Highly Dispersed and Well-Alloyed Bimetallic Nanoparticles on Oxide Support

Supported alloy catalysts play a pivotal role in many heterogeneous catalytic processes of socioeconomic and environmental importance. But the controlled synthesis of supported alloy nanoparticles with consistent composition and tight size distribution remains a challenging issue. Herein, a simple yet effective method for preparation of highly dispersed, homogeneously alloyed bimetallic nanoparticles on oxide supports is reported. This method is based on solid solution of metal cations in parent oxide and strong electrostatic adsorption of a secondary metal species onto the oxide surface. In the reductive annealing process, hydrogen spillover occurs from the surface metal with a higher reduction potential to the solute metal in solid solution, leading to metal exsolution and homogenous alloying of the metals on the oxide surface. The ceria-supported Ni-Pt alloy is chosen as a model catalyst and hydrazine monohydrate decomposition is chosen as a probe reaction to demonstrate this method, and particularly its advantages over the conventional impregnation and galvanic replacement methods. A systematic application of this method using different oxides and base-noble metal pairs further elucidates its applicability and generality.

Unconventional Pressure-Driven Metamagnetic Transitions in Topological van der Waals Magnets

Activating metamagnetic transitions between ordered states in van der Waals magnets and devices bring great opportunities in spintronics. We show that external pressure, which enhances the interlayer hopping without introducing chemical disorders, triggers multiple metamagnetic transitions upon cooling in the topological van der Waals magnets Mn(Bi_1-xSb_x)₄Te₇, where the antiferromagnetic interlayer superexchange coupling competes with the ferromagnetic interlayer coupling mediated by the antisite Mn spins. The temperature-pressure phase diagrams reveal that while the ordering temperature from the paramagnetic to ordered states is almost pressure-independent, the metamagnetic transitions show nontrivial pressure and temperature dependence, even re-entrance. For these highly anisotropic magnets, we attribute the former to the ordering temperature being only weakly dependent on the intralayer parameters and the latter to the parametrically different pressure and temperature dependence of the two interlayer couplings. Our independent probing of these disparate magnetic interactions paves an avenue for efficient magnetic manipulations in van der Waals magnets.

Emerging Optical Microscopy Techniques for Electrochemistry

An optical microscope is probably the most intuitive, simple, and commonly used instrument to observe objects and discuss behaviors through images. Although the idea of imaging electrochemical processes operando by optical microscopy was initiated 40 years ago, it was not until significant progress was made in the last two decades in advanced optical microscopy or plasmonics that it could become a mainstream electroanalytical strategy. This review illustrates the potential of different optical microscopies to visualize and quantify local electrochemical processes with unprecedented temporal and spatial resolution (below the diffraction limit), up to the single object level with subnanoparticle or single-molecule sensitivity. Developed through optically and electrochemically active model systems, optical microscopy is now shifting to materials and configurations focused on real-world electrochemical applications.

High Resolution of Plasmonic Resonance Scattering Imaging with Deep Learning

The dark-field microscopy (DFM) imaging technology has the advantage of a high signal-to-noise ratio, and it is often used for real-time monitoring of plasmonic resonance scattering and biological imaging at the single-nanoparticle level. Due to the limitation of the optical diffraction limit, it is still a challenging task to accurately distinguish two or more nanoparticles whose distance is less than the diffraction limit. Here, we propose a computational strategy based on a deep learning framework (NanoNet), which will realize the effective segmentation of the scattered light spots in diffraction-limited DFM images and obtain high-resolution plasmonic light scattering imaging. A small data set of DFM and the corresponding scanning electron microscopy (SEM) image pairs are used to learn for obtaining a highly resolved semantic imaging model using NanoNet, and thus highly resolved DFM images matching the resolution of those acquired using SEM can be obtained. Our method has the ability to transform diffraction-limited DFM images to highly resolved ones without adding a complex optical system. As a proof of concept, a highly resolved DFM image of living cells through the NanoNet technique is successfully made, opening up a new avenue for high-resolution optical nanoscopic imaging.

In situ lattice tuning of quasi-single-crystal surfaces for continuous electrochemical modulation

The ability to control the atomic-level structure of a solid represents a straightforward strategy for fabricating high-performance catalysts and semiconductor materials. Herein we explore the capability of the mechanically controllable surface strain method in adjusting the surface structure of a gold film. Underpotential deposition measurements provide a quantitative and ultrasensitive approach for monitoring the evolution of surface structures. The electrochemical activities of the quasi-single-crystalline gold films are enhanced productively by controlling the surface tension, resulting in a more positive potential for copper deposition. Our method provides an effective way to tune the atom arrangement of solid surfaces with sub-angstrom precision and to achieve a reduction in power consumption, which has vast applications in electrocatalysis, molecular electronics, and materials science.

We reported a new method capable of adjusting the lattice structure of solid surfaces with sub-angstrom precision and achieved in situ and continuous control over electrochemical activity.

Monitoring the electrochemical reactions on a gold nanoelectrode array via fluorescence-enabled electrochemical imaging

We first observed the electrochemical reaction on an independent gold nanoelectrode via super-resolution fluorescence imaging achieved by anchoring fluorescent molecules on the surface of the nanoelectrode and using an ionic liquid as the electrolyte.

Single-nanoparticle spectroelectrochemistry studies enabled by localized surface plasmon resonance

This review describes recent progress of spectroelectrochemistry (SEC) analysis of single metallic nanoparticles (NPs) which have strong surface plasmon resonance properties. Dark-field scattering (DFS), photoluminescence (PL), and electrogenerated chemiluminescence (ECL) are three commonly used optical methods to detect individual NPs and investigate their local redox activities in an electrochemical cell. These SEC methods are highly dependent on a strong light-scattering cross-section of plasmonic metals and their electrocatalytic characteristics. The surface chemistry and the catalyzed reaction mechanism of single NPs and their chemical transformations can be studied using these SEC methods. Recent progress in the experimental design and fundamental understanding of single-NP electrochemistry and catalyzed reactions using DFS, PL, and ECL is described along with selected examples from recent publications in this field. Perspectives on the challenges and possible solutions for these SEC methods and potential new directions are discussed.

Weak Reaction Scatterometry of Plasmonic Resonance Light Scattering with Machine Learning

Weak reactions are usually overlooked due to weak detectable features and susceptibility to interference from noise signals. Strategies for detecting weak reactions are essential for exploring reaction mechanisms and exploiting potential applications. Machine learning has recently been successfully used to identify patterns and trends in the data. Here, it is demonstrated that machine learning-based techniques can offer accurate local surface plasmon resonance (LSPR) scatterometry by improving the precision of the plasmonic scattering imaging in weak chemical reactions. Dark-field microscopy (DFM) imaging technique is the most effective method for high-sensitivity plasmonic nanoparticles LSPR scatterometry. Unfortunately, deviations caused by the instrument and operating errors are inevitable, and it is difficult to effectively detect the presence of weak reactions. Thus, introducing a machine learning calibration model to automatically calibrate the scattering signal of the nanoprobe in the reaction process can greatly improve the confidence of LSPR scatterometry under DFM imaging and allow DFM imaging to effectively monitor unobvious or weak reactions. By this approach, the weak oxidation of silver nanoparticles (AgNPs) in water by dissolved oxygen was successfully monitored. Moreover, a trivial reaction between AgNPs and mercury ions was detected in a dilute mercury solution with a concentration greater than 1.0 × 10^-10 mol/L. These results suggest the great potential of the combination of LSPR scatterometry and machine learning as a method for imaging analysis and intelligent sensing.

Deciphering Competitive Routes for Nickel-Based Nanoparticle Electrodeposition by an Operando Optical Monitoring

Electrodeposition of earth-abundant iron group metals such as nickel is difficult to characterize by simple electrochemical analyses since the reduction of their metal salts often competes with inhibiting reactions. This makes the mechanistic interpretation sometimes contradictory, preventing unambiguous predictions about the nature and structure of the electrodeposited material. Herein, the complexity of Ni nanoparticles (NPs) electrodeposition on indium tin oxide (ITO) is unraveled operando and at a single entity NP level by optical microscopy correlated to ex situ SEM imaging. Our correlative approach allows differentiating the dynamics of formation of two different NP populations, metallic Ni and Ni(OH)₂ with a <25 nm limit of detection, their formation being ruled by the competition between Ni²⁺ and water reduction. At the single NP level this results in a self-terminated growth, an information which is most often hidden in ensemble averaged measurements.

Alkaline Phosphatase-Triggered Etching of Au@FeOOH Nanoparticles for Enzyme Level Assay under Dark-Field Microscopy

In clinical diagnosis, the level of biological enzymes in serum has been generally regarded as markers of human diseases. In this work, a kind of simple and sensitive plasmonic probe (indicated as Au@FeOOH) has been synthesized with the guidance of plasmonic imaging and subsequently developed for the alkaline phosphatase (ALP) level detection under dark-field microscopy (DFM). As a kind of hydrolysis enzyme, ALP can promote the hydrolysis of l-ascorbic acid 2-phosphate to ascorbic acid (AA). AA further acts as a strong reduction reagent for the decomposition of the FeOOH shell, which results in a blue shift of localized surface plasmon resonance spectra and an obvious color change under DFM. RGB analyses show that using a ΔR/G value instead of scattering wavelength or R/G value as the analytical signal, the deviation attributed to the size distribution of the initial Au NPs is greatly suppressed, and a linear range from 0.2 to 6.0 U/L (R² = 0.99) and a limit of detection of 0.06 U/L are acquired with various concentrations of ALP during the detection. Besides, this approach exhibits excellent selectivity in complex biological serum samples, which is expected to be applied for the early diagnosis of clinical diseases by monitoring various biomarkers in the future.

Single-Particle Hyperspectral Imaging Reveals Kinetics of Silver Ion Leaching from Alloy Nanoparticles

Gold-silver alloy nanoparticles are interesting for multiple applications, including heterogeneous catalysis, optical sensing, and antimicrobial properties. The inert element gold acts as a stabilizer for silver to prevent particle corrosion, or conversely, to control the release kinetics of antimicrobial silver ions for long-term efficiency at minimum cytotoxicity. However, little is known about the kinetics of silver ion leaching from bimetallic nanoparticles and how it is correlated with silver content, especially not on a single-particle level. To characterize the kinetics of silver ion release from gold-silver alloy nanoparticles, we employed a combination of electron microscopy and single-particle hyperspectral imaging with an acquisition speed fast enough to capture the irreversible silver ion leaching. Single-particle leaching profiles revealed a reduction in silver ion leaching rate due to the alloying with gold as well as two leaching stages, with a large heterogeneity in rate constants. We modeled the initial leaching stage as a shrinking-particle with a rate constant that exponentially depends on the silver content. The second, slower leaching stage is controlled by the electrochemical oxidation potential of the alloy being steadily increased by the change in relative gold content and diffusion of silver atoms through the lattice. Interestingly, individual nanoparticles with similar sizes and compositions exhibited completely different silver ion leaching yields. Most nanoparticles released silver completely, but 25% of them appeared to arrest leaching. Additionally, nanoparticles became slightly porous. Alloy nanoparticles, produced by scalable laser ablation in liquid, together with kinetic studies of silver ion leaching, provide an approach to design the durability or bioactivity of alloy nanoparticles.

Orientation-independent reaction activity monitoring with single particle and data analytics

Single-particle analysis is the most powerful method to obtain accurate local information for understanding and monitoring chemical reactions. However, investigations about obtaining comprehensive information at the single-particle level to overcome individual errors and sampling randomness have not been reported to date. Plasmonic nanorods, which have excellent anisotropic optical and chemical properties, make us in situ acquisition of conformation and dynamics of the biological information. On the basis of their anisotropic optical properties of the plasmonic nanorods such as Au nanorods (AuNRs) and data analytics, herein we developed a high-throughput resonance scattering imaging method of AuNRs under dark-field microscopy (DFM) to monitor orientation-independent reaction activity of AuNRs. Data analytics are introduced to determine a large number of AuNRs orientation obtained from a series of polarized DFM images, allowing us to real-time monitor reaction activity of AuNRs at all orientations, and also makes it possible to study the global and local reaction processes of AuNRs at single-particle level. Our method is expected to provide a new strategy for analytical study and single-particle sensing in chemistry.

Surface engineering of Rh-modified Pd nanocrystals by colloidal underpotential deposition for electrocatalytic methanol oxidation

The development of methods to control the surface structures of metallic nanocatalysts is of vital importance for their application as heterogeneous catalysts in chemical conversions of energy and environmental and chemical engineering. The underpotential deposition (UPD) phenomenon has received considerable interest as a tool for the controllable synthesis of metal nanocrystals and engineering their catalytic performances. Herein, the discovery of UPD of Rh on Pd nanocrystals is reported. More importantly, the UPD of Rh is explored as a strategy to direct the synthesis of Rh-modified Pd nanocrystals with controllable shapes and surface structures. The mechanism of the UPD of Rh on Pd is elucidated in terms of electronegativity difference considerations. Compared with pristine Pd octahedral nanocrystals and commercial carbon-supported Pd catalysts, the Rh-modified Pd octahedral nanocrystals exhibit remarkable electrocatalytic performances during the methanol oxidation reaction in alkaline media. Our discovery heralds a new paradigm for UPD-mediated growth of metal nanocrystals and may provide a mechanistic understanding for the guided design of other colloidal UPD systems in the synthesis and surface engineering of metal nanocrystals.

Automated Plasmonic Resonance Scattering Imaging Analysis via Deep Learning

Plasmonic nanoparticles, which have excellent local surface plasmon resonance (LSPR) optical and chemical properties, have been widely used in biology, chemistry, and photonics. The single-particle light scattering dark-field microscopy (DFM) imaging technique based on a color-coded analytical method is a promising approach for high-throughput plasmonic nanoparticle scatterometry. Due to the interference of high noise levels, accurately extracting real scattering light of plasmonic nanoparticles in living cells is still a challenging task, which hinders its application for intracellular analysis. Herein, we propose an automatic and high-throughput LSPR scatterometry technique using a U-Net convolutional deep learning neural network. We use the deep neural networks to recognize the scattering light of nanoparticles from background interference signals in living cells, which have a dynamic and complicated environment, and construct a DFM image semantic analytical model based on the U-Net convolutional neural network. Compared with traditional methods, this method can achieve higher accuracy, stronger generalization ability, and robustness. As a proof of concept, the change of intracellular cytochrome c in MCF-7 cells under UV light-induced apoptosis was monitored through the fast and high-throughput analysis of the plasmonic nanoparticle scattering light, providing a new strategy for scatterometry study and imaging analysis in chemistry.

Nanoscale optical imaging in chemistry

Single-molecule-level measurements are bringing about a revolution in our understanding of chemical and biochemical processes. Conventional measurements are performed on large ensembles of molecules. Such ensemble-averaged measurements mask molecular-level dynamics and static and dynamic fluctuations in reactivity, which are vital to a holistic understanding of chemical reactions. Watching reactions on the single-molecule level provides access to this otherwise hidden information. Sub-diffraction-limited spatial resolution fluorescence imaging methods, which have been successful in the field of biophysics, have been applied to study chemical processes on single-nanoparticle and single-molecule levels, bringing us new mechanistic insights into physiochemical processes. However, the scope of chemical processes that can be studied using fluorescence imaging is considerably limited; the chemical reaction has to be designed such that it involves fluorophores or fluorogenic probes. In this article, we review optical imaging modalities alternative to fluorescence imaging, which expand greatly the range of chemical processes that can be probed with nanoscale or even single-molecule resolution. First, we show that the luminosity, wavelength, and intermittency of solid-state photoluminescence (PL) can be used to probe chemical transformations on the single-nanoparticle-level. Next, we highlight case studies where localized surface plasmon resonance (LSPR) scattering is used for tracking solid-state, interfacial, and near-field-driven chemical reactions occurring in individual nanoscale locations. Third, we explore the utility of surface- and tip-enhanced Raman scattering to monitor individual bond-dissociation and bond-formation events occurring locally in chemical reactions on surfaces. Each example has yielded some new understanding about molecular mechanisms or location-to-location heterogeneity in chemical activity. The review finishes with new and complementary tools that are expected to further enhance the scope of knowledge attainable through nanometer-scale resolution chemical imaging.