ALBERT

Description: Because of their low cost, photovoltaic (PV) cells made from upgraded metallurgical grade silicon (UMG-Si) are a promising alternative to conventional solar grade silicon-based PV cells. This study investigates the outdoor performance of a 1.26 kW grid-connected UMG-Si PV system over five years, reporting the energy yields and performance ratio and estimating the long-term performance degradation rate. To make this investigation more meaningful, the performance of a mono-Si PV system installed at the same place and studied during the same period of time is presented for reference. Furthermore, this study systematizes and rationalizes the necessity of a data selection and filtering process to improve the accuracy of degradation rate estimation. The impact of plane-of-array irradiation threshold for data filtering on performance ratio and degradation rate is also studied. The UMG-Si PV system’s monthly performance ratio after data filtering ranged from 84% to 93% over the observation period. The annual degradation rate was 0.44% derived from time series of monthly performance ratio using the classical decomposition method. A comparison of performance ratio and degradation rate to conventional crystalline silicon-based PV systems suggests that performance of the UMG-Si PV system is comparable to that of conventional systems.

Description: This paper reports a versatile nano-sensor technology using “top-down” poly-silicon nanowire field-effect transistors (FETs) in the conventional Complementary Metal-Oxide Semiconductor (CMOS)-compatible semiconductor process. The nanowire manufacturing technique reduced nanowire width scaling to 50 nm without use of extra lithography equipment, and exhibited superior device uniformity. These n type polysilicon nanowire FETs have positive pH sensitivity (100 mV/pH) and sensitive deoxyribonucleic acid (DNA) detection ability (100 pM) at normal system operation voltages. Specially designed oxide-nitride-oxide buried oxide nanowire realizes an electrically Vth-adjustable sensor to compensate device variation. These nanowire FETs also enable non-volatile memory application for a large and steady Vth adjustment window (〉2 V Programming/Erasing window). The CMOS-compatible manufacturing technique of polysilicon nanowire FETs offers a possible solution for commercial System-on-Chip biosensor application, which enables portable physiology monitoring and in situ recording.

Description: This paper reports a versatile nano-sensor technology using “top-down” poly-silicon nanowire field-effect transistors (FETs) in the conventional Complementary Metal-Oxide Semiconductor (CMOS)-compatible semiconductor process. The nanowire manufacturing technique reduced nanowire width scaling to 50 nm without use of extra lithography equipment, and exhibited superior device uniformity. These n type polysilicon nanowire FETs have positive pH sensitivity (100 mV/pH) and sensitive deoxyribonucleic acid (DNA) detection ability (100 pM) at normal system operation voltages. Specially designed oxide-nitride-oxide buried oxide nanowire realizes an electrically Vth-adjustable sensor to compensate device variation. These nanowire FETs also enable non-volatile memory application for a large and steady Vth adjustment window (〉2 V Programming/Erasing window). The CMOS-compatible manufacturing technique of polysilicon nanowire FETs offers a possible solution for commercial System-on-Chip biosensor application, which enables portable physiology monitoring and in situ recording.

Description: Materials, Vol. 11, Pages 932: Study on Epoxy Resin Toughened by Epoxidized Hydroxy-Terminated Polybutadiene Materials doi: 10.3390/ma11060932 Authors: Zhen Ge Wenguo Zhang Chao Huang Yunjun Luo Epoxy resin (EP) was toughened by epoxidized hydroxy-terminated polybutadiene (EHTPB), with the corresponding modified epoxy resin being prepared. In this paper, the microstructure of EHTPB-modified epoxy resin was characterized, while the influence of different contents of EHTPB on curing kinetics, mechanical properties, morphology, thermal properties, dynamic thermomechanical (DMA) properties and crosslink density of the modified epoxy resin were also discussed. The results showed that the EHTPB-modified epoxy resin was successfully prepared and cured completely. The activation energy (Ea) of the modified epoxy resin decreased after the addition of EHTPB. With an increase in the EHTPB content, the tensile strength (σm) of the modified epoxy resin decreased and the breaking elongation (εb) increased gradually. The initial decomposition temperature (T5%) and glass transition temperature (Tg) of the modified epoxy resin decreased with an increase in the EHTPB content. The modified epoxy resin had a rough fractured surface and the interface was blurred, presenting a ductile fracture.

Description: Sustainability, Vol. 10, Pages 1071: Long-Term Impacts of China’s New Commercial Harvest Exclusion Policy on Ecosystem Services and Biodiversity in the Temperate Forests of Northeast China Sustainability doi: 10.3390/su10041071 Authors: Kai Liu Yu Liang Hong He Wen Wang Chao Huang Shengwei Zong Lei Wang Jiangtao Xiao Haibo Du Temperate forests in Northeast China have been severely exploited by timber harvesting in the last century. To reverse this trend, China implemented the Classified Forest Management policy in the Natural Forest Conservation Program in 1998 to protect forests from excessive harvesting. However, the policy was unable to meet the 2020 commitment of increasing growing stock (set in the Kyoto Protocol) because of high-intensity harvesting. Accordingly, China banned all commercial harvesting in Northeast China in 2014. In this study, we investigated the long-term impacts of the no commercial harvest (NCH) policy on ecosystem services and biodiversity using a forest landscape model, LANDIS PRO 7.0, in the temperate forests of the Small Khingan Mountains, Northeast China. We designed three management scenarios: The H scenario (the Classified Forest Management policy used in the past), the NCH scenario (the current Commercial Harvest Exclusion policy), and the LT scenario (mitigation management, i.e., light thinning). We compared total aboveground forest biomass, biomass by tree species, abundance of old-growth forests, and diversity of tree species and age class in three scenarios from 2010 to 2100. We found that compared with the H scenario, the NCH scenario increased aboveground forest biomass, abundance of old-growth forests, and biomass of most timber species over time; however, it decreased the biomass of rare and protected tree species and biodiversity. We found that the LT scenario increased the biomass of rare and protected tree species and biodiversity in comparison with the NCH scenario, while it maintained aboveground forest biomass and abundance of old-growth forests at a high level (slightly less than the NCH scenario). We concluded there was trade-off between carbon storage and biodiversity. We also concluded that light thinning treatment was able to regulate the trade-off and alleviate the negative effects associated with the NCH policy. Our results highlighted limitations of the NCH policy and provided new insights into sustainable forest management and the interdependence between human society and the forest ecosystem.

Description: Epileptic seizure detection is commonly implemented by expert clinicians with visual observation of electroencephalography (EEG) signals, which tends to be time consuming and sensitive to bias. The epileptic detection in most previous research suffers from low power and unsuitability for processing large datasets. Therefore, a computerized epileptic seizure detection method is highly required to eradicate the aforementioned problems, expedite epilepsy research and aid medical professionals. In this work, we propose an automatic epilepsy diagnosis framework based on the combination of multi-domain feature extraction and nonlinear analysis of EEG signals. Firstly, EEG signals are pre-processed by using the wavelet threshold method to remove the artifacts. We then extract representative features in the time domain, frequency domain, time-frequency domain and nonlinear analysis features based on the information theory. These features are further extracted in five frequency sub-bands based on the clinical interest, and the dimension of the original feature space is then reduced by using both a principal component analysis and an analysis of variance. Furthermore, the optimal combination of the extracted features is identified and evaluated via different classifiers for the epileptic seizure detection of EEG signals. Finally, the performance of the proposed method is investigated by using a public EEG database at the University Hospital Bonn, Germany. Experimental results demonstrate that the proposed epileptic seizure detection method can achieve a high average accuracy of 99.25%, indicating a powerful method in the detection and classification of epileptic seizures. The proposed seizure detection scheme is thus hoped to eliminate the burden of expert clinicians when they are processing a large number of data by visual observation and to speed-up the epilepsy diagnosis.