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Metal oxides are promising (photo)electrocatalysts for sustainable energy technologies due to their good activity and abundant resources. Their applications such as photocatalytic water splitting predominantly involve aqueous interfaces under electrochemical conditions, but in situ probing oxide-water interfaces is proven to be extremely challenging. Here, we present an electrochemical scanning tunneling microscopy (EC-STM) study on the rutile TiO2(110)-water interface, and by tuning surface redox chemistry with careful potential control we are able to obtain high quality images of interfacial structures with atomic details. It is interesting to find that the interfacial water exhibits an unexpected double-row pattern that has never been observed. This finding is confirmed by performing a large scale simulation of a stepped interface model enabled by machine learning accelerated molecular dynamics (MLMD) with ab initio accuracy. Furthermore, we show that this pattern is induced by the steps present on the surface, which can propagate across the terraces through interfacial hydrogen bonds. Our work demonstrates that by combining EC-STM and MLMD we can obtain new atomic details of interfacial structures that are valuable to understand the activity of oxides under realistic conditions.
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The microscopic structure of the material's solid-liquid interface significantly influences its physicochemical properties. Peak force infrared microscopy (PFIR) is a powerful technique for analyzing these interfaces at the nanoscale, revealing crucial structure-activity relationships. PFIR is recognized for its explicit photothermal signal generation mechanism but tends to overlook other photoinduced forces, which can disturb the obtained infrared spectra, thereby reducing spectral signal-to-noise ratio (SNR) and sensitivity. We have developed a multiphysics-coupled theoretical model to assess the magnitudes of various photoinduced forces in PFIR experiments and have found that the magnitude of the photoacoustic force is comparable to that of the photothermal expansion force in a liquid environment. Our calculations show that through simple modulation of the pulse waveform it is possible to effectively suppress the photoacoustic interference, thereby improving the SNR and sensitivity of PFIR. This work aims to alert researchers to the potential for strong photoacoustic interference in liquid-phase PFIR measurements and enhance the performance of PFIR by clarifying the photoinduced forces entangled in the signals.
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Contact Electro-Catalysis (CEC) using commercial dielectric materials in contact-separation cycles with water triggers interfacial electron transfer, generating reactive oxygen species (ROS). However, the hydrophobicity of these materials limits reaction sites, and the generated ROS often combine to form hydrogen peroxide (H2O2), which does not decompose further, leading to suboptimal rates. Addressing H2O2 generation and activation is crucial for advancing CEC. Here, we synthesized a catalyst by loading polytetrafluoroethylene (PTFE) onto ZSM-5 (PZ), achieving uniform dispersion in water. Introducing an FeIII-initiated self-cycling Fenton system (SF-CEC), with synergistic O2 activation and FeIII-activated H2O2, enhanced ROS generation. This system enabled nearly 99% degradation of azo dyes within 10 minutes, a sixfold improvement over traditional CEC. It represents the fastest ultrasound-induced degradation rate of methyl orange dye to date. Without extra oxidants, it also achieved stable dissolution of precious metals in weakly acidic solutions at room temperature, with 80% gold dissolution within 2 hours-2.5 times faster than similar systems. This study corrects the perception of CEC under acidic conditions, offering new insights for dye degradation and precious metal recovery.
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Single cell amperometry has proven to be a powerful and well-established method for characterizing single vesicular exocytotic events elicited at the level of excitable cells under various experimental conditions. Nevertheless, most of the reported characteristics are descriptive, being mostly concerned with the morphological characteristics of the recorded current spikes (maximum current intensities, released charge, rise and fall times, etc.) which are certainly important but do not provide sufficient kinetic information on exocytotic mechanisms due to lack of quantitative models. Here, continuing our previous efforts to provide rigorous models rationalizing the kinetic structures of frequently encountered spike types (spikes with unique exponential decay tails and kiss-and-run events), we describe a new theoretical approach enabling a quantitative kinetic modeling of all types of exocytotic events giving rise to current spikes exhibiting exponential decay tails. This model follows directly from the fact that the condensation of long intravesicular polyelectrolytic strands by high concentrations of monocationic neurotransmitter molecules leads to a matrix structure involving two compartments in constant kinetic exchanges during release. This kinetic model has been validated theoretically (direct and inverse problems) and its experimental interest established by the analysis of the amperometric spikes relative to chromaffin and PC12 cells previously published by some of us.
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Plasmon-mediated chemical reactions (PMCR) have garnered growing interest as a promising concept for photocatalysis. However, in electrochemical systems at solid-liquid interfaces, the photo-induced charge transfer on the surface of metal-semiconductor heterostructures involves complex processes and mechanisms, which are still poorly understood. We explore the plasmon-mediated carrier transfer mechanism and the synergistic effect of light and electric fields on Ag-TiO2 heterostructures, through a combination of electrochemical surface-enhanced Raman spectroscopy and photoelectrochemical methods, with para-aminothiophenol (PATP) serving as a probe molecule. The results show that photocurrent responses are dependent on not only excitation wavelengths and applied potentials, but also the irreversibility of redox. The relationship between photocurrent responses and the chemical transformation between PATP and 4,4'-dimercaptoazobenzene is established, reflecting the photo-induced charge transfer of the heterostructures. The collaboration of spectroscopic and photoelectrochemical methods provide valuable insights into the chemical transformation and kinetic information of adsorbed molecules on the heterostructure during PMCR, offering opportunities for modulating of photocatalytic activities of hot carriers.
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Precisely modulating the kinetics of toehold-mediated DNA strand displacements (TMSD) is essential for its application in DNA nanotechnology. The sequence in the toehold region significantly influences the kinetics of TMSD. However, due to the large sample space resulting from various arrangements of base sequences and the resulted complex secondary structures, such a correlation is not intuitive. Herein, machine learning was employed to reveal the relationship between the kinetics of TMSD and the toehold sequence as well as the correlated secondary structure of invader strands. Key factors that influence the rate constant of TMSD were identified, such as the number of free hydrogen bonding sites in the invader, the number of free bases in the toehold, and the number of hydrogen bonds in intermediates. Moreover, a predictive model was constructed, which successfully achieved semi-quantitative prediction of rate constants of TMSD even with subtle distinctions in toehold sequence.
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The emerging field of nanoscale infrared (nano-IR) offers label-free molecular contrast, yet its imaging speed is limited by point-by-point traverse acquisition of a three-dimensional (3D) data cube. Here, we develop a spatial-spectral network (SS-Net), a miniaturized deep-learning model, together with compressive sampling to accelerate the nano-IR imaging. The compressive sampling is performed in both the spatial and spectral domains to accelerate the imaging process. The SS-Net is trained to learn the mapping from small nano-IR image patches to the corresponding spectra. With this elaborated mapping strategy, the training can be finished quickly within several minutes using the subsampled data, eliminating the need for a large-labeled dataset of common deep learning methods. We also designed an efficient loss function, which incorporates the image and spectral similarity to enhance the training. We first validate the SS-Net on an open stimulated Raman-scattering dataset; the results exhibit the potential of 10-fold imaging speed improvement with state-of-the-art performance. We then demonstrate the versatility of this approach on atomic force microscopy infrared (AFM-IR) microscopy with 7-fold imaging speed improvement, even on nanoscale Fourier transform infrared (nano-FTIR) microscopy with up to 261.6 folds faster imaging speed. We further showcase the generalization of this method on AFM-force volume-based multiparametric nanoimaging. This method establishes a paradigm for rapid nano-IR imaging, opening new possibilities for cutting-edge research in materials, photonics, and beyond.
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Fast and efficient sample pretreatment is the prerequisite for realizing surface-enhanced Raman spectroscopy (SERS) detection of trace targets in complex matrices, which is still a big issue for the practical application of SERS. Recently, we have proposed a highly performed liquid-liquid extraction (LLE)-back extraction (BE) for weak acids/bases extraction in drinking water and beverage samples. However, the performance efficiency decreased drastically on facing matrices like food and biological blood. Based on the total interaction energies among target, interferent, and extractant molecules, solid-phase extraction (SPE) with a higher selectivity was introduced in advance of LLE-BE, which enabled the sensitive (µg L-1 level) and rapid (within 10 min) SERS detection of both koumine (a weak base) and celastrol (a weak acid) in different food and biological samples. Further, the high SERS sensitivity was determined unmanned by Vis-CAD (a machine learning algorithm), instead of the highly demanded expert recognition. The generality of SPE-LLE-BE for various weak acids/bases (2 < pKa < 12), accompanied by the high efficiency, easy operation, and low cost, offers SERS as a powerful on-site and efficient inspection tool in food safety and forensics.
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Extração em Fase Sólida , Análise Espectral Raman , Análise Espectral Raman/métodos , Extração Líquido-Líquido , Humanos , Triterpenos Pentacíclicos , Análise de Alimentos/métodos , Nanopartículas Metálicas/químicaRESUMO
Due to the superior catalytic activity and efficient utilization of noble metals, nanocatalysts are extensively used in the modern industrial production of chemicals. The surface structures of these materials are significantly influenced by reactive adsorbates, leading to dynamic behavior under experimental conditions. The dynamic nature poses significant challenges in studying the structure-activity relations of catalysts. Herein, we unveil an anomalous entropic effect on catalysis via surface pre-melting of nanoclusters through machine learning accelerated molecular dynamics and free energy calculation. We find that due to the pre-melting of shell atoms, there exists a non-linear variation in the catalytic activity of the nanoclusters with temperature. Consequently, two notable changes in catalyst activity occur at the respective temperatures of melting for the shell and core atoms. We further study the nanoclusters with surface point defects, i.e. vacancy and ad-atom, and observe significant decrease in the surface melting temperatures of the nanoclusters, enabling the reaction to take place under more favorable and milder conditions. These findings not only provide novel insights into dynamic catalysis of nanoclusters but also offer new understanding of the role of point defects in catalytic processes.
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Unconventional 1T'-phase transition metal dichalcogenides (TMDs) have aroused tremendous research interest due to their unique phase-dependent physicochemical properties and applications. However, due to the metastable nature of 1T'-TMDs, the controlled synthesis of 1T'-TMD monolayers (MLs) with high phase purity and stability still remains a challenge. Here we report that 4H-Au nanowires (NWs), when used as templates, can induce the quasi-epitaxial growth of high-phase-purity and stable 1T'-TMD MLs, including WS2, WSe2, MoS2 and MoSe2, via a facile and rapid wet-chemical method. The as-synthesized 4H-Au@1T'-TMD core-shell NWs can be used for ultrasensitive surface-enhanced Raman scattering (SERS) detection. For instance, the 4H-Au@1T'-WS2 NWs have achieved attomole-level SERS detections of Rhodamine 6G and a variety of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike proteins. This work provides insights into the preparation of high-phase-purity and stable 1T'-TMD MLs on metal substrates or templates, showing great potential in various promising applications.
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Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for highly sensitive qualitative and quantitative analyses of trace targets. However, sensitive SERS detection can only be facilitated with a suitable sample pretreatment in fields related to trace amounts for food safety and clinical diagnosis. Currently, the sample pretreatment for SERS detection is normally borrowed and improved from the ones in the lab, which yields a high recovery but is tedious and time-consuming. Rapid detection of trace targets in a complex environment is still a considerable issue for SERS detection. Herein, we proposed a liquid-liquid extraction method coupled with a back-extraction method for sample pretreatment based on the pH-sensitive reversible phase transition of the weak organic acids and bases, where the lowest detectable concentrations were identical before and after the pretreatment process. The sensitive (µg L-1 level) and rapid (within 5 min) SERS detection of either koumine, a weak base, or celastrol, a weak acid, was demonstrated in different drinking water samples and beverages. Furthermore, target generality was demonstrated for a variety of weak acids and bases (2 < pKa < 12), and the hydrophilicity/hydrophobicity of the target determines the pretreatment efficiency. Therefore, the LLE-BE coupled SERS was developed as an easy, rapid, and low-cost tool for the trace detection of the two types of targets in simple matrices, which paved the way toward trace targets in complex matrices.
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Água Potável , Análise Espectral Raman , Análise Espectral Raman/métodos , Bebidas , Extração Líquido-LíquidoRESUMO
Copper is widely used in everyday life and industrial production because of its good electrical and thermal conductivity. To overcome copper oxidation and maintain its good physical properties, small organic molecules adsorbed on the surface of copper make a passivated layer to further avoid copper corrosion. In this work, we have investigated thioglycolic acid (TGA, another name is mercaptoacetic acid) adsorbed on copper surfaces by using density functional theory (DFT) calculations and a periodical slab model. We first get five stable adsorption structures, and the binding interaction between TGA and Cu(111) surfaces by using density of states (DOS), indicating that the most stable configuration adopts a triple-end binding model. Then, we analyze the vibrational Raman spectra of TGA adsorbed on the Cu(111) surface and make vibrational assignments according to the vibrational vectors. Finally, we explore the temperature effect of the thermodynamically Gibbs free energy of TGA on the Cu(111) surface and the antioxidant ability of the small organic molecular layer of copper oxidation on the copper surface. Our calculated results further provide evidences to interpret the stability of adsorption structures and antioxidant properties of copper.
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There is growing interest in developing a high-performance self-supervised denoising algorithm for real-time chemical hyperspectral imaging. With a good understanding of the working function of the zero-shot Noise2Noise-based denoising algorithm, we developed a self-supervised Signal2Signal (S2S) algorithm for real-time denoising with a single chemical hyperspectral image. Owing to the accurate distinction and capture of the weak signal from the random fluctuating noise, S2S displays excellent denoising performance, even for the hyperspectral image with a spectral signal-to-noise ratio (SNR) as low as 1.12. Under this condition, both the image clarity and the spatial resolution could be significantly improved and present an almost identical pattern with a spectral SNR of 7.87. The feasibility of real-time denoising during imaging was well demonstrated, and S2S was applied to monitor the photoinduced exfoliation of transition metal dichalcogenide, which is hard to accomplish by confocal Raman spectroscopy. In general, the real-time denoising capability of S2S offers an easy way toward in situ/in vivo/operando research with much improved spatial and temporal resolution. S2S is open-source at https://github.com/3331822w/Signal2signal and will be accessible online at https://ramancloud.xmu.edu.cn/tutorial.
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Palladium hydrides (PdHx) are pivotal in both fundamental research and practical applications across a wide spectrum. PdHx nanocrystals, synthesized by heating in dimethylformamide (DMF), exhibit remarkable stability, granting them widespread applications in the field of electrocatalysis. However, this stability appears inconsistent with their metastable nature. The substantial challenges in characterizing nanoscale structures contribute to the limited understanding of this anomalous phenomenon. Here, through a series of well-conceived experimental designs and advanced characterization techniques, including aberration-corrected scanning transmission electron microscopy (AC-STEM), in situ X-ray diffraction (XRD), and time-of-flight secondary ion mass spectrometry (TOF-SIMS), we have uncovered evidence that indicates the presence of C and N within the lattice of Pd (PdCxNy), rather than H (PdHx). By combining theoretical calculations, we have thoroughly studied the potential configurations and thermodynamic stability of PdCxNy, demonstrating a 2.5:1 ratio of C to N infiltration into the Pd lattice. Furthermore, we successfully modulated the electronic structure of Pd nanocrystals through C and N doping, enhancing their catalytic activity in methanol oxidation reactions. This breakthrough provides a new perspective on the structure and composition of Pd-based nanocrystals infused with light elements, paving the way for the development of advanced catalytic materials in the future.
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The conversion of methane under ambient conditions has attracted significant attention. Although advancements have been made using active oxygen species from photo- and electro- chemical processes, challenges such as complex catalyst design, costly oxidants, and unwanted byproducts remain. This study exploits the concept of contact-electro-catalysis, initiating chemical reactions through charge exchange at a solid-liquid interface, to report a novel process for directly converting methane under ambient conditions. Utilizing the electrification of commercially available Fluorinated Ethylene Propylene (FEP) with water under ultrasound, we demonstrate how this interaction promote the activation of methane and oxygen molecules. Our results show that the yield of HCHO and CH3OH can reach 467.5 and 151.2â µmol â gcat -1, respectively. We utilized electron paramagnetic resonance (EPR) to confirm the evolution of hydroxyl radicals (â OH) and superoxide radicals (â OOH). Isotope mass spectrometry (MS) was employed to analyze the elemental origin of CH3OH, which can be further oxidized to HCHO. Additionally, we conducted density functional theory (DFT) simulations to assess the reaction energies of FEP with H2O, O2, and CH4 under these conditions. The implications of this methodology, with its potential applicability to a wider array of gas-phase catalytic reactions, underscore a significant advance in catalysis.
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Sixty years ago, Reddy, Devanatan, and Bockris performed the first in situ electrochemical ellipsometry experiment, which ushered in a new era in the study of electrochemistry, using optical spectroscopy. After six decades of development, electrochemical optical spectroscopy, particularly electrochemical vibrational spectroscopy, has advanced from a phase of immaturity with few methods and limited applications to a phase of maturity with excellent substrate generality and significantly improved resolutions. Here, we divide the development of electrochemical optical spectroscopy into four phases, focusing on the proof-of-concept of different electrochemical optical spectroscopy studies, the emergence of plasmonic enhancement-based electrochemical optical spectroscopic (in particular vibrational spectroscopic) methods, the realization of electrochemical vibrational spectroscopy on well-defined surfaces, and the efforts to achieve operando spectroelectrochemical applications. Finally, we discuss the future development trend of electrochemical optical spectroscopy, as well as examples of new methodology and research paradigms for operando spectroelectrochemistry.
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Denoising is a necessary step in image analysis to extract weak signals, especially those hardly identified by the naked eye. Unlike the data-driven deep-learning denoising algorithms relying on a clean image as the reference, Noise2Noise (N2N) was able to denoise the noise image, providing sufficiently noise images with the same subject but randomly distributed noise. Further, by introducing data augmentation to create a big data set and regularization to prevent model overfitting, zero-shot N2N-based denoising was proposed in which only a single noisy image was needed. Although various N2N-based denoising algorithms have been developed with high performance, their complicated black box operation prevented the lightweight. Therefore, to reveal the working function of the zero-shot N2N-based algorithm, we proposed a lightweight Peak2Peak algorithm (P2P) and qualitatively and quantitatively analyzed its denoising behavior on the 1D spectrum and 2D image. We found that the high-performance denoising originates from the trade-off balance between the loss function and regularization in the denoising module, where regularization is the switch of denoising. Meanwhile, the signal extraction is mainly from the self-supervised characteristic learning in the data augmentation module. Further, the lightweight P2P improved the denoising speed by at least ten times but with little performance loss, compared with that of the current N2N-based algorithms. In general, the visualization of P2P provides a reference for revealing the working function of zero-shot N2N-based algorithms, which would pave the way for the application of these algorithms toward real-time (in situ, in vivo, and operando) research improving both temporal and spatial resolutions. The P2P is open-source at https://github.com/3331822w/Peak2Peakand will be accessible online access at https://ramancloud.xmu.edu.cn/tutorial.
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Charged microdroplets offer novel electrochemical environments, distinct from traditional solid-liquid or solid-liquid-gas interfaces, due to the intense electric fields at liquid-gas interfaces. In this study, we propose that charged microdroplets serve as microelectrochemical cells (MECs), enabling unique electrochemical reactions at the gas-liquid interface. Using electrospray-generated microdroplets, we achieved multielectron CO2 reduction and C-C coupling to synthesize ethanol using molecular catalysts. These catalysts effectively harness and relay electrons, enhancing the longevity of solvated electrons and enabling multielectron reactions. Importantly, we revealed the intrinsic relationship between the size and charge density of a MEC and its reaction selectivity. Employing in situ mass spectrometry, we identified reaction intermediates (molecular catalyst adducts with HCOO) and oxidation products, elucidating the CO2 reduction mechanism and the comprehensive reaction procedure. Our research underscores the promising role of charged microdroplets in pioneering new electrochemical systems.