By comparing optical spectral results of both Sn-rich and Sn-poor Cu2ZnSnS4 (CZTS) with the previously calculated defect levels, we confirm that the band-tail states in CZTS originate from the high concentration of 2CuZn + SnZn defect clusters, whereas the deep-donor states originate from the high concentration of SnZn. In Sn-rich CZTS, the absorption, reflectance, and photocurrent (PC) spectra show band-tail states that shrink the bandgap to only ∼1.34 eV, while photoluminescence (PL) and PC spectra consistently show that abundant CuZn + SnZn donor states produce a PL peak at ∼1.17 eV and abundant SnZn deep-donor states produce a PL peak near 0.85 eV. In contrast, Sn-poor CZTS shows neither bandgap shrinking nor any deep-donor-defect induced PL and PC signals. These results highlight that a Sn-poor composition is critical for the reduction of band-tailing effects and deep-donor defects and thus the overcoming of the severe open-circuit voltage (V oc) deficiency problem in CZTS solar cells.

ObjectivesIt remains unclear whether non-alcoholic fatty liver disease (NAFLD) is a cause or a consequence of metabolic syndrome (MetS). We proposed a simplified Bayesian network (BN) and attempted to confirm their reciprocal causality.SettingBidirectional longitudinal cohorts (subcohorts A and B) were designed and followed up from 2005 to 2011 based on a large-scale health check-up in a Chinese population.ParticipantsSubcohort A (from NAFLD to MetS, n=8426) included the participants with or without NAFLD at baseline to follow-up the incidence of MetS, while subcohort B (from MetS to NAFLD, n=16 110) included the participants with or without MetS at baseline to follow-up the incidence of NAFLD.ResultsIncidence densities were 2.47 and 17.39 per 100 person-years in subcohorts A and B, respectively. Generalised estimating equation analyses demonstrated that NAFLD was a potential causal factor for MetS (relative risk, RR, 95% CI 5.23, 3.50 to 7.81), while MetS was also a factor for NAFLD (2.55, 2.23 to 2.92). A BN with 5 simplification strategies was used for the reciprocal causal inference. The BN's causal inference illustrated that the total effect of NAFLD on MetS (attributable risks, AR%) was 2.49%, while it was 19.92% for MetS on NAFLD. The total effect of NAFLD on MetS components was different, with dyslipidemia having the greatest (AR%, 10.15%), followed by obesity (7.63%), diabetes (3.90%) and hypertension (3.51%). Similar patterns were inferred for MetS components on NAFLD, with obesity having the greatest (16.37%) effect, followed by diabetes (10.85%), dyslipidemia (10.74%) and hypertension (7.36%). Furthermore, the most important causal pathway from NAFLD to MetS was that NAFLD led to elevated GGT, then to MetS components, while the dominant causal pathway from MetS to NAFLD began with dyslipidaemia.ConclusionsThe findings suggest a reciprocal causality between NAFLD and MetS, and the effect of MetS on NAFLD is significantly greater than that of NAFLD on MetS.

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In molecular dissociative ionization by electron collisions and dissociative electron attachment to molecule, the respective positively and negatively charged fragments are the important products. A compact ion velocity mapping apparatus is developed for the angular distribution measurements of the positive or negative fragments produced in the electron-molecule reactions. This apparatus consists of a pulsed electron gun, a set of ion velocity mapping optic lenses, a two-dimensional position detector including two pieces of micro-channel plates, and a phosphor screen, and a charge-coupled-device camera for data acquisition. The positive and negative ion detections can be simply realized by changing the voltage polarity of ion optics and detector. Velocity sliced images can be directly recorded using a narrow voltage pulse applied on the rear micro-channel plate. The efficient performance of this system is evaluated by measuring the angular distribution of O(-) from the electron attachments to NO at 7.3 and 8.3 eV and O(+) from the electron collision with CO at 40.0 eV.

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BackgroundHand, foot, and mouth disease (HFMD) is the most common communicable disease in China. Shandong Province is one of the most seriously affected areas. The distribution of HFMD had spatial heterogeneity and seasonal characteristic in this setting. The aim of this study was to explore the associations between climate and HFMD by a Bayesian approach from spatio-temporal interactions perspective.MethodsThe HFMD data of Shandong Province during 2008–2012 were derived from the China National Disease Surveillance Reporting and Management System. And six climatic indicators were obtained from the Meteorological Bureau of Shandong Province. The global spatial autocorrelation statistic (Moran’s I) was used to detect the spatial autocorrelation of HFMD cases in each year. The optimal one among four Bayesian models was further adopted to estimate the relative risk of the occurrence of HFMD via Markov chain Monte Carlo.ResultsThe annual average incidence rate of HFMD was 104.40 per 100,000 in Shandong Province. Positive spatial autocorrelation appeared at county level (Moran’s I ≥0.30, P < 0.001). The best fitting Spatio-temporal interactive model showed that annual average temperature, annual average pressure, annual average relative humidity, annual average wind speed and annual sunshine hours were significantly positive related to the occurrence of HFMD. The estimated relative risk of 36, 87, 91, 79, 65 out of 140 counties for 2008–2012 respectively were significantly more than 1.ConclusionsThere were obvious spatio-temporal heterogeneity of HFMD in Shandong Province, and the climatic indicators were associated with the epidemic of HFMD. Bayesian approach should be recommended to capture the spatial-temporal pattern of HFMD.

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