Copper (I) iodide (CuI) films are grown on glass substrates with a direct vacuum thermal evaporation method, and the effect of substrate temperature on their photoluminescence and transparent conductive properties is discussed. The X‐ray diffraction (XRD) measurement identifies the polycrystalline CuI film has γ‐phase with (111) preferential growth direction. When the substrate temperature is optimised at 120 °C, the average transmittance is about 90% in the wavelength range of 410–1500 nm. The electrical properties measured by Hall effect show the lowest resistivity of 1.0 × 10−2 Ωcm with hole concentration of 3.0 × 1019 cm−3 and mobility of 25 cm2 V−1s−1. These results indicate that direct thermal deposition is a simple method to grow high quality p‐type CuI films.

Controlling the interface quality and surface microstructure of the cuprous oxide (Cu 2 O) p−n homojunction is crucial to obtaining high-efficiency film solar cells. However, the low-cost synthetic techniques for preparing such homojunction structures with a high-quality interface and designed surface microstructure still remain a challenge because of the doping difficulty for the n-type of Cu 2 O, especially with aqueous precursors. Herein, we report an electrochemical deposition approach to growing the Cu 2 O p− n homojunction by selecting proper electrolytes of different pH levels. The Cu 2 O film growth orientation and surface microstructure are controlled by adjusting the applied deposition potential and the precursor concentration. The epitaxial growth of the Cu 2 O homojunction with n/p films of the same crystal orientation reduces the interface states and the formation of a textured structure on the surface helps the photons to be absorbed more effectively, which both enhance the photovoltaic conversion efficiency of Cu 2 O film solar cells. Our findings provide an effective method for the fabrication of Cu 2 O homojunctions with a high-quality interface and textured surface, which can pave the way to further improve the photovoltaic properties of Cu 2 O-based film solar cell devices.

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Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18-24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.