In this work, the study of photocatalytic hydrogen production by RGO/ZnIn2S4 with simultaneous degradation of organic amines was carried out in the presence of organic amines in wastewater as the sacrificial agents. The effects of several factors, such as organic amine types, pH value, catalyst concentration, organic amine concentration, and sunlight source, on the photocatalytic activity of RGO/ZnIn2S4 for H2 production were investigated. At the same time, its performance of degrading organic amines during H2 production was also examined. The results showed that the order of H2 production activity of RGO/ZnIn2S4 in six organic amine solutions was N(CH2CH3)3 > N(HOCH2CH2)3 > N(CH3)3 > HO(CH2)2NH2 > C6H5–N2> CO(NH2)2, and the highest H2 production was in N(CH2CH3)3 (triethylamine) solution, being 1597 μmol·g–1·h–1, which is 2.6 times as high as that using the aqueous solution mixture of Na2S and Na2SO3 as the sacrificial agent. In addition, when the pH was 13, the catalyst concentration was 1.0 g·L–1, and the triethylamine concentration was 1.0 mol·L–1, the photocatalytic activity was the highest. Furthermore, the relationship between triethylamine concentration and H2 production was analyzed according to the theory of dynamics.
The burgeoning fields of artificial intelligence and bio-inspired robotics necessitate the development of advanced artificial synapses that transcend the limitations of conventional unitary and rigid systems. This study presents a self-powered, flexible, multi-sensory artificial synaptic device based on a heterojunction structure of reduced graphene oxide (rGO) and sulfur vacancy-rich ZnIn 2 S 4 (Sv-ZIS), which ingeniously integrates optical perception and tactile sensing capabilities within a single platform. By engineering interfacial voids and defect states, the dynamic photo-carrier trapping and releasing enable the emulation of fundamental synaptic behaviors under optical stimulation, with relaxation time reaching 200 s. Concurrently, the device exhibits a quantifiable mechanical response to strain and external force, originating from the contact potential difference between the Sv-ZIS and rGO layers. Remarkably, all functionalities are achieved at zero bias, ensuring minimal energy consumption. Leveraging the multi-sensory ability, an artificial neural network is constructed for intelligent object recognition based on their optical transmittance and weight characteristics, achieving a high accuracy of 97.67%. This work establishes a novel paradigm for the development of energy-efficient, flexible, and multi-modal neuromorphic devices, paving the way for advanced applications in soft robotics, wearable electronics, and human-machine interfaces.
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