[Study on Si-PIN and CdTe Detectors Used in Energy Dispersive X-Ray Fluorescence Measurements].

Semiconductor detector is widely used in energy dispersive X-ray fluorescence measurements due to its excellent performance. In this paper, Si-PIN and CdTe semiconductor detectors were studied, performances of the two detectors were compared in material properties, detection efficiency, energy resolution and other aspects. Focused on the performance of the detectors influenced by the thickness of detector sensitive area, energy of incident X-ray, shaping time of post-stage circuit, and analyzed the differences of energy spectrum caused by escape peaks and hole trailing. Aiming at the problem of incomplete hole collection in detector, a digital multi-channel analyzer (DMCA) based on FPGA with rise-time discriminator was designed, it could reduce the influence of hole trailing effectively and improve energy resolution. The experimentation results indicate that the detection efficiency of Si-PIN and CdTe is roughly equal when energy is below 15 keV while CdTe has much higher detection efficiency than Si-PIN when energy is above 15 keV. The optimum forming time of the Si-PIN detector is about 10 µs, and the CdTe detector is about 2.6 µs, so the CdTe detector is more suitable for the high count rate condition. Si-PIN detector has better energy resolution than CdTe detector for different energy incident X-ray. CdTe detector has obvious hole tailing effect and the energy resolution of CdTe detector is significantly improved by using DMCA with rise-time discrimination.

Simulation of crop growth, time to maturity and yield by an improved sigmoidal model.

Models that accurately estimate maximum crop biomass to obtain a reliable forecast of yield are useful in crop improvement programs and aiding establishment of government policies, including those addressing issues of food security. Here, we present a new sigmoidal growth model (NSG) and compare its performance with the beta sigmoidal growth model (BSG) for capturing the growth trajectories of eight crop species. Results indicated that both the NSG and the BSG fitted all the growth datasets well (R2 > 0.98). However, the NSG performed better than the BSG based on the calculated value of Akaike's information criterion (AIC). The NSG provided a consistent estimate for when maximum biomass occurred; this suggests that the parameters of the BSG may have less biological importance as compared to those in the NSG. In summary, the new sigmoidal growth model is superior to the beta sigmoidal growth model, which can be applied to capture the growth trajectory of various plant species regardless of the initial biomass values at the beginning of a growth period. Findings of this study will be helpful to understand the growth trajectory of different plant species regardless of their initial biomass values at the beginning of a growth period.