Wet Pretreatment-Induced Modification of Cu(In,Ga)Se2/Cd-Free ZnTiO Buffer Interface.

We report a novel Cd-free ZnTiO buffer layer deposited by atomic layer deposition for Cu(In,Ga)Se2 (CIGS) solar cells. Wet pretreatments of the CIGS absorbers with NH4OH, H2O, and/or aqueous solution of Cd2+ ions were explored to improve the quality of the CIGS/ZnTiO interface, and their effects on the chemical state of the absorber and the final performance of Cd-free CIGS devices were investigated. X-ray photoelectron spectroscopy (XPS) analysis revealed that the aqueous solution etched away sodium compounds accumulated on the CIGS surface, which was found to be detrimental for solar cell operation. Wet treatment with NH4OH solution led to a reduced photocurrent, which was attributed to the thinning (or removal) of an ordered vacancy compound (OVC) layer on the CIGS surface as evidenced by an increased Cu XPS peak intensity after the NH4OH treatment. However, the addition of Cd2+ ions to the NH4OH aqueous solution suppressed the etching of the OVC by NH4OH, explaining why such a negative effect of NH4OH is not present in the conventional chemical bath deposition of CdS. The band alignment at the CIGS/ZnTiO interface was quantified using XPS depth profile measurements. A small cliff-like conduction band offset of -0.11 eV was identified at the interface, which indicates room for further improvement of efficiency of the CIGS/ZnTiO solar cells once the band alignment is altered to a slight spike by inserting a passivation layer with a higher conduction band edge than ZnTiO. Combination of the small cliff conduction band offset at the interface, removal of the Na compound via water, and surface doping by Cd ions allowed the application of ZnTiO buffer to CIGS treated with Cd solutions, exhibiting an efficiency of 80% compared to that of a reference CIGS solar cell treated with the CdS.

Estimation of sleep posture using a patch-type accelerometer based device.

In this study, we developed a sleep posture estimation algorithm using 3-axis accelerometer signals measured from a patch-type sensor. Firstly, we inspected the characteristics of accelerometer signals for different sleep postures. Based on the results, we established decision rules to estimate 5 postures containing supine, left, right lateral, prone postures, and non-sleep postures such as sitting and standing. The algorithm was tested by the data from thirteen subjects during night time PSG. As a result, the algorithm estimated sleep postures with an average agreement of 99.16%, and cohen's kappa of 0.98 compared with reference sleep postures determined by position sensor and video recording. The proposed method with the device could be used as supportive purpose in routine PSG study and out-of-hospital environment.

Sleep/wake estimation using only anterior tibialis electromyography data.

BACKGROUND: In sleep efficiency monitoring system, actigraphy is the simplest and most commonly used device. However, low specificity to wakefulness of actigraphy was revealed in previous studies. In this study, we assumed that sleep/wake estimation using actigraphy and electromyography (EMG) signals would show different patterns. Furthermore, each EMG pattern in two states (sleep, wake during sleep) was analysed. Finally, we proposed two types of method for the estimation of sleep/wake patterns using only EMG signals from anterior tibialis muscles and the results were compared with PSG data. METHODS: Seven healthy subjects and five patients (2 obstructive sleep apnea, 3 periodic limb movement disorder) participated in this study. Night time polysomnography (PSG) recordings were conducted, and electrooculogram, EMG, electroencephalogram, electrocardiogram, and respiration data were collected. Time domain analysis and frequency domain analysis were applied to estimate the sleep/wake patterns. Each method was based on changes in amplitude or spectrum (total power) of anterior tibialis electromyography signals during the transition from the sleep state to the wake state. To obtain the results, leave-one-out-cross-validation technique was adopted. RESULTS: Total sleep time of the each group was about 8 hours. For healthy subjects, the mean epoch-by-epoch results between time domain analysis and PSG data were 99%, 71%, 80% and 0.64 (sensitivity, specificity, accuracy and kappa value), respectively. For frequency domain analysis, the corresponding values were 99%, 73%, 81% and 0.67, respectively. Absolute and relative differences between sleep efficiency index from PSG and our methods were 0.8 and 0.8% (for frequency domain analysis). In patients with sleep-related disorder, our proposed methods revealed the substantial agreement (kappa > 0.61) for OSA patients and moderate or fair agreement for PLMD patients. CONCLUSIONS: The results of our proposed methods were comparable to those of PSG. The time and frequency domain analyses showed the similar sleep/wake estimation performance.