This paper examines the effect of five major pretreatments on the surface coverage Γ(m) of dodecanethiol self-assembled monolayer on polycrystalline gold electrode (C(12)SH-SAMs-Au). It is based on the electrochemical reductive desorption in the alkaline solution by cyclic voltammetry (CV). The five different pretreatment methods include: aqua regia pretreatment, reductive annealed pretreatment, UV/O(3) pretreatment, piranha reagents pretreatment and simple polishing pretreatment, and then all above pretreatments following the same procedure of electrochemistry cleaning. The experimental results show that the surface coverage Γ(m) for C(12)SH-SAMs-Au by the five pretreatment methods are: aqua regia pretreatment (8.0 × 10(-10) mol cm(-2)) ~ reductive annealed pretreatment (7.8 × 10(-10) mol cm(-2)) > UV/O(3) pretreatment (5.0 × 10(-10) mol cm(-2)) ~ piranha reagents pretreatment (4.1 × 10(-10) mol cm(-2)) ~ simple polishing pretreatment (4.0 × 10(-10) mol cm(-2)). This indicates that Au surfaces pretreated by aqua regia and reductive annealing can achieve the best results, and the Γ(m) values obtained are consistent with the theoretical coverage values (Γ(m) ≈ 8.0 × 10(-10) mol cm(-2)); however, the Γ(m) values for other three pretreatment methods (UV/O(3), piranha reagents and simple polishing) are not satisfactory, obtaining only almost half of the theoretical Γ(m) value. Thus, we recommend aqua regia and reductive annealed pretreatments as the best methods for self-assembling the alkyl thiol monolayer (C(n)SH-SAMs-Au), whereas UV/O(3), piranha reagents and simple polishing pretreatments are not recommended.

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<p>Session-based recommendation (SBR) systems aim to utilize the user’s short-term behavior sequence to predict the next item without the detailed user profile. Most recent works try to model the user preference by treating the sessions as between-item transition graphs and utilize various graph neural networks (GNNs) to encode the representations of pair-wise relations among items and their neighbors. Some of the existing GNN-based models mainly focus on aggregating information from the view of spatial graph structure, which ignores the temporal relations within neighbors of an item during message passing and the information loss results in a sub-optimal problem. Other works embrace this challenge by incorporating additional temporal information but lack sufficient interaction between the spatial and temporal patterns. To address this issue, inspired by the uniformity and alignment properties of contrastive learning techniques, we propose a novel framework called Session-based Recommendation with Spatio-Temporal Contrastive Learning Enhanced GNNs (RESTC). The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism. Furthermore, a novel global collaborative filtering graph (CFG) embedding is leveraged to enhance the spatial view in the main task. Extensive experiments demonstrate the significant performance of RESTC compared with the state-of-the-art baselines e.g., with an improvement as much as 27.08% gain on HR@20 and 20.10% gain on MRR@20. </p>

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