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In recent years, surface-enhanced Raman scattering (SERS) of a molecule/metal-semiconductor hybrid system has attracted considerable interest and regarded as the synergetic contribution of the electromagnetic and chemical enhancements from the incorporation of noble metal into semiconductor nanomaterials. However, the underlying mechanism is still to be revealed in detail. Herein, we report an irreversible accumulated SERS behavior induced by near-infrared (NIR) light irradiating on a 4-mercaptobenzoic acid linked with silver and silver-doped titanium dioxide (4MBA/Ag/Ag-doped TiO 2) hybrid system. With increasing irradiation time, the SERS intensity of 4MBA shows an irreversible exponential increase, and the Raman signal of the Ag/Ag-doped TiO 2 substrate displays an exponential decrease. A microscopic understanding of the time-dependent SERS behavior is derived based on the microanalysis of the Ag/Ag-doped TiO 2 nanostructure and the molecular dynamics, which is attributed to three factors: (1) higher crystallinity of Ag/Ag-doped TiO 2 substrate; (2) photo-induced charge transfer; (3) chargeinduced molecular reorientation.

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Pesticides, extensively used in agriculture production, have received enormous attention because of their potential threats to the environment and human health. Hence, in this study, a kind of highly sensitive and stable hybrid surface-enhanced Raman scattering (SERS)-active substrates constructed with flower-like two-dimensional molybdenum sulfide and Ag (MoS2@Ag) has been developed, and then the above substrate was sequentially utilized in the recyclable detection of pesticide residues on several kinds of fruits and vegetables. In the first place, the excellent photocatalytic performance of the MoS2@Ag hybrid substrate was demonstrated, which was attributed to the inhibition of electron–hole combination after the formation of Schottky barrier between the Ag NPs and MoS2 matrix. Thereafter, two calibration curves with ultra-low limits of detection (LOD) as 6.4 × 10–7 and 9.8 × 10–7 mg/mL were established for the standard solutions of thiram (tetramethylthiuram disulfide, TMTD) and methyl parathion (MP), and then the recyclable assay of their single and mixed residues on eggplant, Chinese cabbage, grape, and strawberry was successfully realized. It is interesting to note that the detection recoveries from 95.5 to 63.1% for TMTD and 92.3 to 62.6% for MP are greatly dependent on the size and surface roughness of these foods. In a word, the MoS2@Ag composite matrix shows attractive SERS and photocatalysis performance, and it is expected to have the potential application on food safety monitoring.

Efficient high-dimensional performance modeling of today's complex analog and mixed-signal (AMS) circuits with large-scale process variations is an important yet challenging task. In this paper, we propose a novel performance modeling algorithm that is referred to as Bayesian Model Fusion (BMF). Our key idea is to borrow the simulation data generated from an early stage (e.g., schematic level) to facilitate efficient high-dimensional performance modeling at a late stage (e.g., post layout) with low computational cost. Such a goal is achieved by statistically modeling the performance correlation between early and late stages through Bayesian inference. Several circuit examples designed in a commercial 32nm CMOS process demonstrate that BMF achieves up to 9× runtime speedup over the traditional modeling technique without surrendering any accuracy.