Perylene diimide/iron phthalocyanine Z-scheme heterojunction with strong interfacial charge transfer through π-π interaction: Efficient photocatalytic degradation of tetracycline hydrochloride.

The development of an all-organic Z-scheme heterojunction photocatalyst with the matched band structure, efficient electron transfer and excellent photocatalytic performance is valuable for a sustainable future. A novel perylene diimide/phthalocyanine iron (PDI/FePc) heterojunctions with strong π-π interaction were synthesized by a self-assembled method, which exhibited strong visible-light-driven photocatalytic degradation activities of tetracycline hydrochloride (TC). The TC removal rate over PDI/FePc was achieved three times and 87.5 times higher than that of PDI and FePc. PDI/FePc (131.1 mv·dec-1) presented a lower Taffel slope than that of PDI (228.6 mv·dec-1) for the oxidation. This may be due to the strong π-π interactions between PDI and FePc, which can reduce the layer spacing of the supramolecular structure and facilitate the separation and transfer of photogenerated carriers in the built-in electric field. In addition, radical quenching tests revealed that superoxide radicals (•O2-) acted as a dominant role in photocatalytic oxidation. An increscent specific surface area of PDI decorated by FePc also gave the rapid pathway for charge transfer and enhanced the adsorption ability. This provides a new idea for the formation of heterojunction to improve the photocatalytic activity of organic supramolecular materials.

One-Pot Synthesis of N-Doped NiO for Enhanced Photocatalytic CO2 Reduction with Efficient Charge Transfer.

The green and clean sunlight-driven catalytic conversion of CO2 into high-value-added chemicals can simultaneously solve the greenhouse effect and energy problems. The controllable preparation of semiconductor catalyst materials and the study of refined structures are of great significance for the in-depth understanding of solar-energy-conversion technology. In this study, we prepared nitrogen-doped NiO semiconductors using a one-pot molten-salt method. The research shows that the molten-salt system made NiO change from p-type to n-type. In addition, nitrogen doping enhanced the adsorption of CO2 on NiO and increased the separation of photogenerated carriers on the NiO. It synergistically optimized the CO2-reduction system and achieved highly active and selective CO2 photoreduction. The CO yield on the optimal nitrogen-doped photocatalyst was 235 µmol·g-1·h-1 (selectivity 98%), which was 16.8 times that of the p-type NiO and 2.4 times that of the n-type NiO. This can be attributed to the fact that the nitrogen doping enhanced the oxygen vacancies of the NiOs and their ability to adsorb and activate CO2 molecules. Photoelectrochemical characterization also confirmed that the nitrogen-doped NiO had excellent electron -transfer and separation properties. This study provides a reference for improving NiO-based semiconductors for photocatalytic CO2 reduction.

Training set extension for SVM ensemble in P300-speller with familiar face paradigm.

BACKGROUND: P300-spellers are brain-computer interface (BCI)-based character input systems. Support vector machine (SVM) ensembles are trained with large-scale training sets and used as classifiers in these systems. However, the required large-scale training data necessitate a prolonged collection time for each subject, which results in data collected toward the end of the period being contaminated by the subject's fatigue. OBJECTIVE: This study aimed to develop a method for acquiring more training data based on a collected small training set. METHODS: A new method was developed in which two corresponding training datasets in two sequences are superposed and averaged to extend the training set. The proposed method was tested offline on a P300-speller with the familiar face paradigm. RESULTS: The SVM ensemble with extended training set achieved 85% classification accuracy for the averaged results of four sequences, and 100% for 11 sequences in the P300-speller. In contrast, the conventional SVM ensemble with non-extended training set achieved only 65% accuracy for four sequences, and 92% for 11 sequences. CONCLUSION: The SVM ensemble with extended training set achieves higher classification accuracies than the conventional SVM ensemble, which verifies that the proposed method effectively improves the classification performance of BCI P300-spellers, thus enhancing their practicality.