It is widely accepted that the sensitivity of surface-enhanced Raman spectroscopy (SERS) is mainly manipulated by the electromagnetic enhancement mechanism (EM). Herein, we determined that the direct adsorption of the target on the SERS active surface is vital as well, through the systematic investigation of the SERS behavior of three positively charged molecules on negatively charged gold (Au) or silver nanoparticles (Ag NPs). Facilitated by the synergistic effect among the molecule, the surface, and the specific adsorbed halide ions (Cl − , Br − , and I − ), high SERS sensitivity for trace target was realized, which was mainly from the directly adsorbed molecules. Noteworthy, little contribution from the nondirectly adsorbed molecules was discernible, although the EM enhancement was at the same level for these two surface species dwelling within a distance significantly less than 1 nm from the surface. Further, the related strategy for trace detection sheds light on how to realize sensitive SERS detection of new targets.
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.
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.
Deep learning has been widely used in image processing, quantitative analysis, and other applications in optical-resolution photoacoustic microscopy (OR-PAM). It requires a large amount of photoacoustic data for training and testing. However, due to the complex structure, high cost, slow imaging speed, and other factors of OR-PAM, it is difficult to obtain enough data required by deep learning, which limits the research of deep learning in OR-PAM to a certain extent. To solve this problem, a virtual OR-PAM based on k-Wave is proposed. The virtual photoacoustic microscopy mainly includes the setting of excitation light source and ultrasonic probe, scanning and signal processing, which can realize the common Gaussian-beam and Bessel-beam OR-PAMs. The system performance (lateral resolution, axial resolution, and depth of field) was tested by imaging a vertically tilted fiber, and the effectiveness and feasibility of the virtual simulation platform were verified by 3D imaging of the virtual vascular network. The ability to the generation of the dataset for deep learning was also verified. The construction of the virtual OR-PAM can promote the research of OR-PAM and the application of deep learning in OR-PAM.
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