In situ Raman spectroscopy reveals the structure and dissociation of interfacial water.

Understanding the structure and dynamic process of water at the solid-liquid interface is an extremely important topic in surface science, energy science and catalysis1-3. As model catalysts, atomically flat single-crystal electrodes exhibit well-defined surface and electric field properties, and therefore may be used to elucidate the relationship between structure and electrocatalytic activity at the atomic level4,5. Hence, studying interfacial water behaviour on single-crystal surfaces provides a framework for understanding electrocatalysis6,7. However, interfacial water is notoriously difficult to probe owing to interference from bulk water and the complexity of interfacial environments8. Here, we use electrochemical, in situ Raman spectroscopic and computational techniques to investigate the interfacial water on atomically flat Pd single-crystal surfaces. Direct spectral evidence reveals that interfacial water consists of hydrogen-bonded and hydrated Na+ ion water. At hydrogen evolution reaction (HER) potentials, dynamic changes in the structure of interfacial water were observed from a random distribution to an ordered structure due to bias potential and Na+ ion cooperation. Structurally ordered interfacial water facilitated high-efficiency electron transfer across the interface, resulting in higher HER rates. The electrolytes and electrode surface effects on interfacial water were also probed and found to affect water structure. Therefore, through local cation tuning strategies, we anticipate that these results may be generalized to enable ordered interfacial water to improve electrocatalytic reaction rates.

1T'-transition metal dichalcogenide monolayers stabilized on 4H-Au nanowires for ultrasensitive SERS detection.

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.

A general method for rapid synthesis of refractory carbides by low-pressure carbothermal shock reduction.

Refractory carbides are attractive candidates for support materials in heterogeneous catalysis because of their high thermal, chemical, and mechanical stability. However, the industrial applications of refractory carbides, especially silicon carbide (SiC), are greatly hampered by their low surface area and harsh synthetic conditions, typically have a very limited surface area (<200 m2 g-1), and are prepared in a high-temperature environment (>1,400 °C) that lasts for several or even tens of hours. Based on Le Chatelier's principle, we theoretically proposed and experimentally verified that a low-pressure carbothermal reduction (CR) strategy was capable of synthesizing high-surface area SiC (569.9 m2 g-1) at a lower temperature and a faster rate (∼1,300 °C, 50 Pa, 30 s). Such high-surface area SiC possesses excellent thermal stability and antioxidant capacity since it maintained stability under a water-saturated airflow at 650 °C for 100 h. Furthermore, we demonstrated the feasibility of our strategy for scale-up production of high-surface area SiC (460.6 m2 g-1), with a yield larger than 12 g in one experiment, by virtue of an industrial viable vacuum sintering furnace. Importantly, our strategy is  also applicable to the rapid synthesis of refractory metal carbides (NbC, Mo2C, TaC, WC) and even their emerging high-entropy carbides (VNbMoTaWC5, TiVNbTaWC5). Therefore, our low-pressure CR method provides an alternative strategy, not merely limited to temperature and time items, to regulate the synthesis and facilitate the upcoming industrial applications of carbide-based advanced functional materials.

Sixty years of electrochemical optical spectroscopy: a retrospective.

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.

What can molecular assembly learn from catalysed assembly in living organisms?

Molecular assembly is the process of organizing individual molecules into larger structures and complex systems. The self-assembly approach is predominantly utilized in creating artificial molecular assemblies, and was believed to be the primary mode of molecular assembly in living organisms as well. However, it has been shown that the assembly of many biological complexes is "catalysed" by other molecules, rather than relying solely on self-assembly. In this review, we summarize these catalysed-assembly (catassembly) phenomena in living organisms and systematically analyse their mechanisms. We then expand on these phenomena and discuss related concepts, including catalysed-disassembly and catalysed-reassembly. Catassembly proves to be an efficient and highly selective strategy for synergistically controlling and manipulating various noncovalent interactions, especially in hierarchical molecular assemblies. Overreliance on self-assembly may, to some extent, hinder the advancement of artificial molecular assembly with powerful features. Furthermore, inspired by the biological catassembly phenomena, we propose guidelines for designing artificial catassembly systems and developing characterization and theoretical methods, and review pioneering works along this new direction. Overall, this approach may broaden and deepen our understanding of molecular assembly, enabling the construction and control of intelligent assembly systems with advanced functionality.

Charged Microdroplets as Microelectrochemical Cells for CO2 Reduction and C-C Coupling.

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.

What Elements Really Intercalate into Pd Lattice When Heated in Dimethylformamide?

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.

Revealing the Denoising Principle of Zero-Shot N2N-Based Algorithm from 1D Spectrum to 2D Image.

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.

Fast Nano-IR Hyperspectral Imaging Empowered by Large-Dataset-Free Miniaturized Spatial-Spectral Network.

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.

Rapid Sample Pretreatment Facilitating SERS Detection of Trace Weak Organic Acids/Bases in Complex Matrices.

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.

Signal2signal: Pushing the Spatiotemporal Resolution to the Limit by Single Chemical Hyperspectral Imaging.

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.

Adsorption Structures, Vibrational Raman Spectra and Chemical Binding Properties of Thioglycolic Acid on Cu(111) Surfaces: A DFT Study.

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.

Machine Learning Molecular Dynamics Shows Anomalous Entropic Effect on Catalysis through Surface Pre-melting of Nanoclusters.

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.

Contact-Electro-Catalysis for Direct Oxidation of Methane under Ambient Conditions.

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 151.2 µmol ⋅ gcat -1, respectively. We utilized electron paramagnetic resonance (EPR) to confirm the evolution of hydroxyl radicals (⋅OH) and superoxide radicals (⋅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.

Chiral Molecular Cage with Tunable Stereoinversion Barriers.

The manipulation of chirality in molecular entities that rapidly interconvert between enantiomeric forms is challenging, particularly at the supramolecular level. Advances in controlling such dynamic stereochemical systems offer opportunities to understand chiral symmetry breaking and homochirality. Herein, we report the synthesis of a face-rotating tetrahedron (FRT), an organic molecular cage composed of tridurylborane facial units that undergo stereomutations between enantiomeric trefoil propeller-like conformations. After resolution, we show that the racemization barrier of the enantiopure FRT can be regulated in situ through the reversible binding of fluoride anions onto the tridurylborane moieties. Furthermore, the addition of an enantiopure phenylethanol to the FRT can effectively induce chirality of the molecular cage by preferentially binding to one of its enantiomeric conformers. This study presents a new paradigm for controlling dynamic chirality in supramolecular systems, which may have implications for asymmetric synthesis and dynamic stereochemistry.

Improving the Hydrogen Oxidation Reaction Rate of Ru by Active Hydrogen in the Ultrathin Pd Interlayer.

Enhancing the catalytic activity of Ru metal in the hydrogen oxidation reaction (HOR) potential range, improving the insufficient activity of Ru caused by its oxophilicity, is of great significance for reducing the cost of anion exchange membrane fuel cells (AEMFCs). Here, we use Ru grown on Au@Pd as a model system to understand the underlying mechanism for activity improvement by combining direct in situ surface-enhanced Raman spectroscopy (SERS) evidence of the catalytic reaction intermediate (OHad) with in situ X-ray diffraction (XRD), electrochemical characterization, as well as DFT calculations. The results showed that the Au@Pd@Ru nanocatalyst utilizes the hydrogen storage capacity of the Pd interlayer to "temporarily" store the activated hydrogen enriched at the interface, which spontaneously overflows at the "hydrogen-deficient interface" to react with OHad adsorbed on Ru. It is the essential reason for the enhanced catalytic activity of Ru at anodic potential. This work deepens our understanding of the HOR mechanism and provides new ideas for the rational design of advanced electrocatalysts.

Seeing Is Not Necessarily Believing: Is the Surface-Enhanced Raman Spectroscopy Signal Really from the Target?

Reagent purity is crucial to experimental research, considering that the ignorance of ultratrace impurities may induce wrong conclusions in either revealing the reaction nature or qualifying the target. Specifically, in the field of surface science, the strong interaction between the impurity and the surface will bring a non-negligible negative effect. Surface-enhanced Raman spectroscopy (SERS) is a highly surface-sensitive technique, providing fingerprint identification and near-single molecule sensitivity. In the SERS analysis of trace chloromethyl diethyl phosphate (DECMP), we figured out that the SERS performance of DECMP is significantly distorted by the trace impurities from DECMP. With the aid of gas chromatography-based techniques, one strongly interfering impurity (2,2-dichloro-N,N-dimethylacetamide), the byproduct during the synthesis of DECMP, was confirmed. Furthermore, the nonignorable interference of impurities on the SERS measurement of NaBr, NaI, or sulfadiazine was also observed. The generality ignited us to refresh and consolidate the guideline for the reliable SERS qualitative analysis, by which the potential misleading brought by ultratrace impurities, especially those strongly adsorbed on Au or Ag surfaces, could be well excluded.

1D Gradient-Weighted Class Activation Mapping, Visualizing Decision Process of Convolutional Neural Network-Based Models in Spectroscopy Analysis.

Being characterized by the self-adaption and high accuracy, the deep learning-based models have been widely applied in the 1D spectroscopy-related field. However, the "black-box" operation and "end-to-end" working style of the deep learning normally bring the low interpretability, where a reliable visualization is highly demanded. Although there are some well-developed visualization methods, such as Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM), for the 2D image data, they cannot correctly reflect the weights of the model when being applied to the 1D spectral data, where the importance of position information is not considered. Here, aiming at the visualization of Convolutional Neural Network-based models toward the qualitative and quantitative analysis of 1D spectroscopy, we developed a novel visualization algorithm (1D Grad-CAM) to more accurately display the decision-making process of the CNN-based models. Different from the classical Grad-CAM, with the removal of the gradient averaging (GAP) and the ReLU operations, a significantly improved correlation between the gradient and the spectral location and a more comprehensive spectral feature capture were realized for 1D Grad-CAM. Furthermore, the introduction of difference (purity or linearity) and feature contribute in the CNN output in 1D Grad-CAM achieved a reliable evaluation of the qualitative accuracy and quantitative precision of CNN-based models. Facing the qualitative and adulteration quantitative analysis of vegetable oils by the combination of Raman spectroscopy and ResNet, the visualization by 1D Grad-CAM well reflected the origin of the high accuracy and precision brought by ResNet. In general, 1D Grad-CAM provides a clear vision about the judgment criterion of CNN and paves the way for CNN to a broad application in the field of 1D spectroscopy.

Graphene Coated Dielectric Hierarchical Nanostructures for Highly Sensitive Broadband Infrared Sensing.

Broadband infrared (IR) absorption is sought after for wide range of applications. Graphene can support IR plasmonic waves tightly bound to its surface, leading to an intensified near-field. However, the excitation of graphene plasmonic waves usually relies on resonances. Thus, it is still difficult to directly obtain both high near-field intensity and high absorption rate in ultra-broad IR band. Herein, a novel method is proposed to directly realize high near-field intensity in broadband IR band by graphene coated manganous oxide microwires featured hierarchical nanostructures (HNSs-MnO@Gr MWs) both experimentally and theoretically. Both near-field intensity and IR absorption of HNSs-MnO@Gr MWs are enhanced by at least one order of magnitude compared to microwires with smooth surfaces. The results demonstrate that the HNSs-MnO@Gr MWs support vibrational sensing of small organic molecules, covering the whole fingerprint region and function group region. Compared with the graphene-flake-based enhancers, the signal enhancement factors reach a record high of 103 . Furthermore, just a single HNSs-MnO@Gr MW can be constructed to realize sensitively photoresponse with high responsivity (over 3000 V W-1 ) from near-IR to mid-IR. The graphene coated dielectric hierarchical micro/nanoplatform with enhanced near-field intensity is scalable and can harness for potential applications including spectroscopy, optoelectronics, and sensing.

Rapid theoretical method for inverse design on a tip-enhanced Raman spectroscopy (TERS) probe.

Tip-enhanced Raman spectroscopy (TERS) can provide correlated topographic and chemical information at the nanoscale, with great sensitivity and spatial resolution depending on the configuration of the TERS probe. The sensitivity of the TERS probe is largely determined by two effects: the lightning-rod effect and local surface plasmon resonance (LSPR). While 3D numerical simulations have traditionally been used to optimize the TERS probe structure by sweeping two or more parameters, this method is extremely resource-intensive, with computation times growing exponentially as the number of parameters increases. In this work, we propose an alternative rapid theoretical method that reduces computational loading while still achieving effective TERS probe optimization through the inverse design method. By applying this method to optimize a TERS probe with four free-structural parameters, we observed a nearly 1 order of magnitude improvement in enhancement factor (|E/E0|2), in contrast to a parameter sweeping 3D simulation that would take ∼7000 hours of computation. Our method, therefore, shows great promise as a useful tool for designing not only TERS probes but also other near-field optical probes and optical antennas.