The adsorption and thermal decomposition of alkanethiols (R-SH, where R = CH3, C2H5, and C4H9) on Pt(111) were studied with temperature-programmed desorption (TPD) and X-ray photoelectron spectroscopy (XPS) with synchrotron radiation. Dissociation of sulfhydryl hydrogen (RS-H) of alkanethiol results in the formation of alkanethiolate; the extent of dissociation at an adsorption temperature of 110 K depends on the length of the alkyl chain. At small exposure, all chemisorbed CH3SH, C2H5SH, and C4H9SH decompose to desorb hydrogen below 370 K and yield carbon and sulfur on the surface. Desorption of products containing carbon is observed only at large exposure. In thermal decomposition, alkanethiolate is proposed to undergo a stepwise dehydrogenation: R'-CH2S --> R'-CHS --> R'-CS, R' = H, CH3, and C3H7. Further decomposition of the R'-CS intermediate results in desorption of H2 at 400-500 K and leaves carbon and sulfur on the surface. On the basis of TPD and XPS data, we conclude that the density of adsorption of alkanethiol decreases with increasing length of the alkyl chain. C4H9SH is proposed to adsorb mainly with a configuration in which its alkyl group interacts with the surface; this interaction diminishes the density of adsorption of alkanethiols but facilitates dehydrogenation of the alkyl group.

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Introduction: Video-based automatic motion analysis has been employed to identify infant motor development delays. To overcome the limitations of lab-recorded images and training datasets, this study aimed to develop an artificial intelligence (AI) model using videos taken by mobile phone to assess infants’ motor skills. Methods: A total of 270 videos of 41 high-risk infants were taken by parents using a mobile device. Based on the Pull to Sit (PTS) levels from the Hammersmith Motor Evaluation, we set motor skills assessments. The videos included 84 level 0, 106 level 1, and 80 level 3 recordings. We used whole-body pose estimation and three-dimensional transformation with a fuzzy-based approach to develop an AI model. The model was trained with two types of vectors: whole-body skeleton and key points with domain knowledge. Results: The average accuracies of the whole-body skeleton and key point models for level 0 were 77.667% and 88.062%, respectively. The Area Under the ROC curve (AUC) of the whole-body skeleton and key point models for level 3 were 96.049% and 94.333% respectively. Conclusions: An AI model with minimal environmental restrictions can provide a family-centered developmental delay screen and enable the remote monitoring of infants requiring intervention.