Deep Learning-Assisted Spectrum-Structure Correlation: State-of-the-Art and Perspectives.

Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.

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.

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.

Patch-Based Convolutional Encoder: A Deep Learning Algorithm for Spectral Classification Balancing the Local and Global Information.

Molecular vibrational spectroscopies, including infrared absorption and Raman scattering, provide molecular fingerprint information and are powerful tools for qualitative and quantitative analysis. They benefit from the recent development of deep-learning-based algorithms to improve the spectral, spatial, and temporal resolutions. Although a variety of deep-learning-based algorithms, including those to simultaneously extract the global and local spectral features, have been developed for spectral classification, the classification accuracy is still far from satisfactory when the difference becomes very subtle. Here, we developed a lightweight algorithm named patch-based convolutional encoder (PACE), which effectively improved the accuracy of spectral classification by extracting spectral features while balancing local and global information. The local information was captured well by segmenting the spectrum into patches with an appropriate patch size. The global information was extracted by constructing the correlation between different patches with depthwise separable convolutions. In the five open-source spectral data sets, PACE achieved a state-of-the-art performance. The more difficult the classification, the better the performance of PACE, compared with that of residual neural network (ResNet), vision transformer (ViT), and other commonly used deep learning algorithms. PACE helped improve the accuracy to 92.1% in Raman identification of pathogen-derived extracellular vesicles at different physiological states, which is much better than those of ResNet (85.1%) and ViT (86.0%). In general, the precise recognition and extraction of subtle differences offered by PACE are expected to facilitate vibrational spectroscopy to be a powerful tool toward revealing the relevant chemical reaction mechanisms in surface science or realizing the early diagnosis in life science.

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.

Resolving nanostructure and chemistry of solid-electrolyte interphase on lithium anodes by depth-sensitive plasmon-enhanced Raman spectroscopy.

The solid-electrolyte interphase (SEI) plays crucial roles for the reversible operation of lithium metal batteries. However, fundamental understanding of the mechanisms of SEI formation and evolution is still limited. Herein, we develop a depth-sensitive plasmon-enhanced Raman spectroscopy (DS-PERS) method to enable in-situ and nondestructive characterization of the nanostructure and chemistry of SEI, based on synergistic enhancements of localized surface plasmons from nanostructured Cu, shell-isolated Au nanoparticles and Li deposits at different depths. We monitor the sequential formation of SEI in both ether-based and carbonate-based dual-salt electrolytes on a Cu current collector and then on freshly deposited Li, with dramatic chemical reconstruction. The molecular-level insights from the DS-PERS study unravel the profound influences of Li in modifying SEI formation and in turn the roles of SEI in regulating the Li-ion desolvation and the subsequent Li deposition at SEI-coupled interfaces. Last, we develop a cycling protocol that promotes a favorable direct SEI formation route, which significantly enhances the performance of anode-free Li metal batteries.

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.

[Rapid Detection of Trace Enrofloxacin and Ciprofloxacin in Drinking Water by SERS].

In recent years, the abuse of antibiotics has led to the spread and diffusion of antibiotic resistance genes in the environment, which poses a potential threat to the ecosystem and human health. In particular, the related reports of antibiotic contamination in drinking water have aroused great social concerns. Therefore, realizing the rapid detection of trace antibiotics in emergency events has become a research hotspot. Here, in combination with magnetic solid phase extraction (MSPE), we established a rapid detection strategy for ng·L-1 level quinolones in drinking water using surface-enhanced Raman spectroscopy (SERS). With the help of the high enrichment capacity provided by the high adsorption capacity of the magnetic graphene oxide composite nanomaterial (Fe3O4@SiO2-GO), the spiked detection of 1.0 ng·L-1 enrofloxacin (ENR) and 5.0 ng·L-1 ciprofloxacin (CIP) in drinking water was successfully achieved, with recoveries ranging from 77.5% to 91.5%, which met the current requirements of drinking water testing. For environmental water samples such as lake water, the selectivity of extraction materials needs to be further improved due to the strong interference of the complex organic matrix.

Visualization of a Machine Learning Framework toward Highly Sensitive Qualitative Analysis by SERS.

Surface-enhanced Raman spectroscopy (SERS), providing near-single-molecule-level fingerprint information, is a powerful tool for the trace analysis of a target in a complicated matrix and is especially facilitated by the development of modern machine learning algorithms. However, both the high demand of mass data and the low interpretability of the mysterious black-box operation significantly limit the well-trained model to real systems in practical applications. Aiming at these two issues, we constructed a novel machine learning algorithm-based framework (Vis-CAD), integrating visual random forest, characteristic amplifier, and data augmentation. The introduction of data augmentation significantly reduced the requirement of mass data, and the visualization of the random forest clearly presented the captured features, by which one was able to determine the reliability of the algorithm. Taking the trace analysis of individual polycyclic aromatic hydrocarbons in a mixture as an example, a trustworthy accuracy no less than 99% was realized under the optimized condition. The visualization of the algorithm framework distinctly demonstrated that the captured feature was well correlated to the characteristic Raman peaks of each individual. Furthermore, the sensitivity toward the trace individual could be improved by least 1 order of magnitude as compared to that with the naked eye. The proposed algorithm distinguished by the lesser demand of mass data and the visualization of the operation process offers a new way for the indestructible application of machine learning algorithms, which would bring push-to-the-limit sensitivity toward the qualitative and quantitative analysis of trace targets, not only in the field of SERS, but also in the much wider spectroscopy world. It is implemented in the Python programming language and is open-source at https://github.com/3331822w/Vis-CAD.

Rapid qualitative and quantitative analysis of trace aconitum phytotoxin by SERS.

The dual functions of phytotoxin, such as aconitine, with biological activity and toxicity ignited the related food poisoning intentionally or accidentally from time to time. The fast and accurate qualitative analysis is a prerequisite for tracking the source of poisoning and taking correct treatments. Taking the single molecule level sensitivity and molecular fingerprinting of Surface-enhanced Raman spectroscopy (SERS), we developed a highly sensitive and accurate strategy for the trace detection of three structurally similar aconitines (ATs) (aconitine, mesaconitine and hypoaconitine) by employing the 100 nm Ag NPs colloid as the SERS substrate. It was figured out that the lowest detectable concentration is in the level of 5.0 µg/L for these three ATs with the linear range of 5.0-100.0 µg/L. The qualitative and quantitative analysis of trace ATs spiked in various food samples was realized in 3 mins, which demonstrated the SERS based strategy is very promising towards the fast and on-site detection of ATs in the field of food safety or criminal identification.

Qualitative analysis of trace quinolone antibiotics by SERS with fine structure dependent sensitivity.

Antibiotics are widely used in daily life, which has created a global scenario where many pathogenic organisms have become effectively resistant to antibiotics. The abuse or overuse of antibiotics causes significant environmental pollution and even endangers human health. It is well-known that antibiotics' efficacy (toxicity) is determined by molecular structure. Therefore, structure-level qualitative analysis with high sensitivity and accuracy is vitally important. Characterized by fingerprinting recognition, Raman spectroscopy, especially surface-enhanced Raman spectroscopy (SERS), has become an essential qualitative analysis tool in various fields, such as environmental monitoring and food safety. With the exception of chirality, this study completed the qualitative trace analysis of 16 quinolone antibiotics (QNs) with fine molecular structure differences using SERS. The sensitivity was tuned in by one order of magnitude through the different electronegativity and steric hindrances of the slightly changed functional groups in the specific antibiotics. The fine structure dependent sensitivity enables SERS to be a powerful on-site monitoring tool to control the abuse of antibiotics with high toxicity; thus, decreasing the subsequent risk to the environmental ecology and human society.

Improving SERS Sensitivity toward Trace Sulfonamides: The Key Role of Trade-Off Interfacial Interactions among the Target Molecules, Anions, and Cations on the SERS Active Surface.

In recent years, ensuring the rational use and effective control of antibiotics has been a major focus in the eco-environment, which requires an effective monitoring method. However, on-site rapid detection of antibiotics in water environments remains a challenging issue. In this study, surface-enhanced Raman spectroscopy (SERS) was used to systematically achieve selective, rapid, and highly sensitive detection of sulfonamides, based on their fingerprint characteristics. The results show that the trade-off between the competitive and coadsorption behaviors of target molecules and agglomerates (inorganic salts) on the surface of the SERS substrate determines whether the molecules can be detected with high sensitivity. Based on this, the qualitative differentiation and quantitative detection of three structurally similar antibiotics, sulfadiazine, sulfamerazine, and sulfamethazine, were achieved, with the lowest detectable concentration being 1 µg/L for sulfadiazine and 50 µg/L for sulfamerazine and sulfamethazine.

Developing a Peak Extraction and Retention (PEER) Algorithm for Improving the Temporal Resolution of Raman Spectroscopy.

In spectroscopic analysis, push-to-the-limit sensitivity is one of the important topics, particularly when facing the qualitative and quantitative analyses of the trace target. Normally, the effective recognition and extraction of weak signals are the first key steps, for which there has been considerable effort in developing various denoising algorithms for decades. Nevertheless, the lower the signal-to-noise ratio (SNR), the greater the deviation of the peak height and shape during the denoising process. Therefore, we propose a denoising algorithm along with peak extraction and retention (PEER). First, both the first and second derivatives of the Raman spectrum are used to determine Raman peaks with a high SNR whose peak information is kept away from the denoising process. Second, an optimized window smoothing algorithm is applied to the left part of the Raman spectrum, which is combined with the untreated Raman peaks to obtain the denoised Raman spectrum. The PEER algorithm is demonstrated with much better signal extraction and retention and successfully improves the temporal resolution of Raman imaging of a living cell by at least 1 order of magnitude higher than those by traditional algorithms.

The Application of Computed Tomography Perfusion in the Alberta Stroke Program Early Computed Tomography Score for Endovascular Treatment of Acute Ischemic Stroke in the Anterior Circulation.

OBJECTIVE: The present study investigated the predictive value of each perfusion parameter of the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) in CT perfusion (CTP) imaging for the prognosis of endovascular treatment at the time of admission in patients with acute ischemic stroke in the anterior circulation. PATIENTS AND METHODS: The imaging data of 62 patients with acute ischemic stroke in the anterior circulation with an onset time of 6 h or less were retrospectively analyzed. All patients underwent the one-stop whole-brain dynamic volume four-dimensional (4D) CT angiography (CTA)-CTP and cranial magnetic resonance imaging (MRI) within seven days after treatment. The patients were divided into better and worse prognosis groups according to their clinical symptoms, combined with an MRI-ASPECTS score of ≤ 6 within seven days after treatment. The observed perfusion parameters included cerebral blood flow (CBF)-ASPECTS, cerebral blood volume (CBV)-ASPECTS, mean transit time (MTT)-ASPECTS, and time to peak (TTP)-ASPECTS. The difference in ASPECTS scores involving the CTP parameter, as well as diagnostic power, was compared between the two groups of patients. RESULTS: All CTP-ASPECTS scores negatively correlated with clinical prognosis. The higher the CTP-ASPECTS scores preceding treatment in patients with ischemic stroke in the anterior circulation, the better the prognosis. There were statistically significant differences in the scores of CBF-ASPECTS and CBV-ASPECTS between the two groups (P < 0.05). Receiver operating curve (ROC) analysis showed that the parameter with the largest area under the curve (AUC) was the CBF-ASPECTS score (P = 0.003), with a sensitivity of 90.9%, a specificity of 59.1%, and an AUC of 0.806, which was the most valuable prognostic predictor. CONCLUSION: The CBF-ASPECTS score presented significant value as a primary indicator for predicting the outcome of endovascular treatment in patients with acute ischemic stroke in the anterior circulation, and it had good application prospects in clinical practice.

First Report of Phomopsis longicolla Causing Stem Canker of Eggplant in Fujian Province, China.

Eggplant (Solanum melongena L.) is one of the most popular vegetable in China. In July 2019, a serious stem canker disease of eggplant cv. Hangqieyiha has been found in commercial fields in Pingnan County, Fujian Province. The disease incidence ranged from 38% to 72%. The symptoms were found on stems but not on fruits. At first the lesions are small, more or less circular, later becoming elongated, blackish-brown lesions, eventually containing pycnidia. When stem girdling occurs, the shoot above the infected area wilts and dries up. The teleomorph of the fungus has not been encountered in sympotomatic stem. Single-conidial isolate has been obtained by using routine fungal-isolation methods and single-spore purification technique. The fungus was cultivated on potato dextrose agar (PDA), incubated under 12h/12h cycles of light and darkness until sporulation to determine. The fungus initially produced white fluffy aerial hyphae, forming relatively dense concentric pattern colony, which subsequently exhibited yellow-green pigmentation. Pycnidias had globose locules and prominent beaks, which immersed in medium, black, solitary, discoid or irregular. Conidiophores were colorless, separated, branched, 10.0 to 20.0 × 1.0 to 2.5 µm. Alpha-conidia were single-celled, ellipsoidal to fusiform, guttulate, 5.4 to 8.7 × 1.5 to 3.2 µm. Beta-conidia were found occasionally in older stock cultures, hyaline, filiform, hamate, and 17.0 to 26.9 × 0.86 to 1.23 µm. Based on these morphological characters, the fungus was identified as Phomopsis longicolla (Hobbs et al., 1985). The rDNA－ITS of the isolate FAFU01 was amplified with primers ITS1/ ITS4 (TCCGTAGGTGAACCTGCGG/ TCCTCCGCTTATTGATATGC) (White et al., 1990)，and A 578 bp sequence obtained (GenBank Accession No. MW380387 ) was 96% to 98.3% identical to the known sequence of P. longicolla or Diaporthe longicolla in GenBank. For further confirmation, P. longicolla specific primers Phom.I /Phom.II (GAGCTCGCCACTAGATTTCAGGG/GGCGGCCAACCAAACTCTTGT) (Zhang et al., 1997) were used and a 337-bp amplification product was obtained which was previously reported only for P. longicolla, whereas no product was amplified from control. Based on these morphological and molecular characters, the fungus was identified as P. longicolla. In greenhouse tests, each of 35-day-old plants of eggplant cv. Hangqieyihao was maintained in 30-cm-diameter pot. Healthy stem on the plants was wounded by pinpricking. Both wounded and non-wounded stems were inoculated respectively with mycelial plugs (4 mm in diameter) from a 7-day-old PDA culture or PDA medium plugs as controls, with six replicates. The plants were covered with plastic bags to maintain high relative humidity for two days. Four days after inoculation, the plugs were washed from the stems. Thirty-five days after inoculation, canker lesions and small, black pycnidias, which were similar to those in the field, were observed on the surface of non-wounded and wounded healthy stems inoculated with pathogen, whereas all the control stems remained healthy. The fungi was re-isolated from the infected stems of plants and was further confirmed with the species-specific primers. These results confirmed the fungus's pathogenicity. This is the first report of P. longicolla causing stem canker in eggplant in Fujian Province, China.

Revealing unconventional host-guest complexation at nanostructured interface by surface-enhanced Raman spectroscopy.

Interfacial host-guest complexation offers a versatile way to functionalize nanomaterials. However, the complicated interfacial environment and trace amounts of components present at the interface make the study of interfacial complexation very difficult. Herein, taking the advantages of near-single-molecule level sensitivity and molecular fingerprint of surface-enhanced Raman spectroscopy (SERS), we reveal that a cooperative effect between cucurbit[7]uril (CB[7]) and methyl viologen (MV2+2I-) in aggregating Au NPs originates from the cooperative adsorption of halide counter anions I-, MV2+, and CB[7] on Au NPs surface. Moreover, similar SERS peak shifts in the control experiments using CB[n]s but with smaller cavity sizes suggested the occurrence of the same guest complexations among CB[5], CB[6], and CB[7] with MV2+. Hence, an unconventional exclusive complexation model is proposed between CB[7] and MV2+ on the surface of Au NPs, distinct from the well-known 1:1 inclusion complexation model in aqueous solutions. In summary, new insights into the fundamental understanding of host-guest interactions at nanostructured interfaces were obtained by SERS, which might be useful for applications related to host-guest chemistry in engineered nanomaterials.

Single-Molecule Level Rare Events Revealed by Dynamic Surface-Enhanced Raman Spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool to monitor various interfacial behaviors providing molecular level information with high spatial and temporal resolutions. However, it is a challenge to obtain SERS spectra with high quality for analytes having a weak binding affinity with plasmonic nanostructures due to the short dwell time of the analyte on the surface. Here, we employed dynamic SERS, an acquisition method consisting of the rapid acquisition of a series of consecutive SERS spectra, to study the adsorption/desorption behavior of R6G on Ag surfaces. We demonstrated that the signal-noise ratio of SERS spectra of mobile molecules can be improved by dynamic SERS even when the acquisition time cannot catch up with the diffusion time of the molecule. More interestingly, we captured the neutral R6G0 state (spectroscopically different from the dominated positive R6G+ state) of R6G at the single-molecule level, which is a rare molecule event hardly detectable by traditional SERS. Dynamic SERS provides near real-time molecular vibrational information with an improved signal-noise ratio, which opens a new avenue to capture metastable or rare molecule events for the comprehensive understanding of interfacial processes related to catalysis and life science.

In Situ Raman Study of CO Electrooxidation on Pt(hkl) Single-Crystal Surfaces in Acidic Solution.

The adsorption and electrooxidation of CO molecules at well-defined Pt(hkl) single-crystal electrode surfaces is a key step towards addressing catalyst poisoning mechanisms in fuel cells. Herein, we employed in situ electrochemical shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) coupled with theoretical calculation to investigate CO electrooxidation on Pt(hkl) surfaces in acidic solution. We obtained the Raman signal of top- and bridge-site adsorbed CO* molecules on Pt(111) and Pt(100). In contrast, on Pt(110) surfaces only top-site adsorbed CO* was detected during the entire electrooxidation process. Direct spectroscopic evidence for OH* and COOH* species forming on Pt(100) and Pt(111) surfaces was afforded and confirmed subsequently via isotope substitution experiments and DFT calculations. In summary, the formation and adsorption of OH* and COOH* species plays a vital role in expediting the electrooxidation process, which relates with the pre-oxidation peak of CO electrooxidation. This work deepens knowledge of the CO electrooxidation process and provides new perspectives for the design of anti-poisoning and highly effective catalysts.

Experimental and Theoretical Study of Surface-Enhanced Raman Spectra of Sulfadiazine Adsorbed on Nanoscale Gold Colloids.

Sulfadiazine, as a class of antibiotics, has been widely used in the world for decades; however, its surface-enhanced Raman spectra (SERS) on gold colloids are obviously different from ordinary Raman spectra in the solid powder and liquid solution. To explore the reasons for such significant differences, we used density functional theory calculations and normal-mode analysis to investigate the effects of the configuration, conformation, protonation, hydrogen-bonding interaction, and adsorption configurations of sulfadiazine on gold clusters to check these different effects on the vibrational assignments. Our calculated results can be summarized as two points. First, the Raman spectra strongly depend on the configuration, conformation, protonation, and hydrogen bonding of sulfadiazine. Second, the wagging vibration displays a significant vibrational frequency shift and a very strong SERS peak responsible for the observed SERS signal when sulfadiazine is adsorbed on gold clusters through the terminal amino group. This is different from another adsorption configuration through two oxygen atoms of the -SO2NH- group on gold clusters. Finally, we further investigate the potential energy surfaces along the wagging vibration and the binding interaction of -NH2 adsorbed on different sites of gold surfaces.

SEIRAS Study of Chloride-Mediated Polyether Adsorption on Cu.

Surface-enhanced infrared absorption spectroscopy is used to examine the co-adsorption of a selection of polyethers with Cl- under conditions relevant to superconformal Cu electrodeposition in CuSO4-H2SO4 electrolytes. In 0.1 mol/L H2SO4, a potential-dependent mixed SO4 2--H3O+/H2O layer forms on weakly textured (111) Cu thin-film surfaces. With the addition of 1 mmol/L NaCl, the SO4 2--H3O+/H2O adlayer is displaced and rapidly replaced by an ordered halide layer that disrupts the adjacent solvent network, leading to an increase in non-hydrogen-bonded water that makes the interface more hydrophobic. The altered wetting behavior facilitates co-adsorption of polyethers, such as poly(ethylene glycols), polyoxamers, or polyoxamines. Interfacial water is displaced by co-adsorption of the hydrophobic polymer segments on the Cl--terminated surface, while the hydrophilic ether oxygens are available for hydrogen bond formation with the solvent. The combined polyether-Cl- layer serves as an effective suppressor of the Cu electrodeposition reaction by limiting access of Cuaq 2+ to the underlying metal surface. This insight differs from previous work which suggested that polymer adsorption is mediated by Cu+-ether binding.