Nanoporous Au (NPG) prepared by dealloying is one of the most used substrates for surface-enhanced Raman scattering (SERS). The morphology tailoring of the NPG to obtain both ultrafine pores and suitable Au/Ag ratio is of great importance for the acquiring of enhanced SERS performance. Compared with the chemical dealloying, the electrochemical dealloying can tailor the NPG to be more flexible by the additional adjustment of dealloying voltage and current. Thus, further understanding on the morphology evolution of NPG during the electrochemical dealloying to obtain enhanced SERS performance is of great importance. In the presented work, the morphology and composition evolution of the NPG film during the electrochemical dealloying was investigated. NPG films with a stable pore diameter of approximately 11 nm as well as diverse compositions were obtained by electrochemical dealloying an Au-Ag alloy film. The prepared NPG film exhibits an enhanced SERS activity with an enhancement factor (EF) of 7.3 × 106 and an excellent detection limit of 10−9 M. This work provides insights into the morphology and composition evolution of the NPG during the electrochemical dealloying process to obtain enhanced SERS performance.

We describe the concept of feature bias (FB) strategies and compare such strategies with traditional feature selection (FS) for predictive machine learning on a collection of datasets. FS is a common step in many classification and regression tasks. It is necessary because machine learning tools often cannot cope when the data has thousands of attributes. However, the strategy used by FS techniques is essentially binary. It is hoped that most "irrelevant" features are removed prior to the application of machine learning, and that the subsequent machine learning stage will be much faster (since there are fewer features to process) and also more successful (since many features will be removed by FS that seem unimportant for the classification task at hand). However, FS methods typically rely on standard statistical ideas and are unable to guarantee that all and only relevant features remain. A feature bias strategy, on the other hand, is an alternative approach in which we never entirely remove any feature from consideration. Experimental results reveal that FB can greatly improve upon FS for prediction tasks, particularly on poorly correlated datasets. We propose a tentative guideline for choosing an FS or FB strategy based on simply calculated inherent correlation of the dataset.