Factors Affecting Terahertz Emission from InGaN Quantum Wells under Ultrafast Excitation

InGaN quantum wells (QWs) grown on c-plane sapphire substrate experience strain due to the lattice mismatch. The strain generates a strong piezoelectric field in QWs that contributes to THz emission under ultrafast excitation. Physical parameters such as QW width, period number, and Indium concentration can affect the strength of the piezoelectric field and result in THz emission. Experimental parameters such as pump fluence, laser energy, excitation power, pump polarization angle, and incident angle can be tuned to further optimize the THz emission. This review summarizes the effects of physical and experimental parameters of THz emission on InGaN QWs. Comparison and relationship between photoluminescence properties and THz emission in QWs are given, which further explains the origin of THz emission in InGaN QWs

Real-space sampling of terahertz waveforms with sub-nanometer spatial resolution

Terahertz scanning tunneling microscopy (THz-STM) has emerged as a potent technique for probing ultrafast nanoscale dynamics with exceptional spatiotemporal precision, whereby the acquisition of THz near-field waveforms holds paramount significance. While substantial efforts have been dedicated to retrieving the waveform utilizing the photoemission current or a molecular sensor, these methods are challenged by intensive thermal effects or complex sample preparations. In this study, we introduce a universal approach for real-time characterization of THz near-field waveforms within the tunnel junction, achieving sub-nanometer spatial resolution. Utilizing the gating mechanism intrinsic to the STM junction, coherent scanning of a gated strong THz pulse over a weak THz pulse is achieved, facilitating direct measurement of the waveform. Notably, employing a custom-built Carrier-Envelope Phase (CEP) shifter, THz-CEP has been successfully characterized in the tunnel junction. Furthermore, THz spectral imaging through point-to-point sampling of THz waveforms on a triatomic Au (111) step has been demonstrated, highlighting the sub-nanometer spatial resolution of our sampling methodology.Comment: 26 pages and 4 figures for the manuscript; 16 pages and 7 figures for the Supporting Informatio

Ultrabroadband terahertz conductivity of highly doped ZnO and ITO

The broadband complex conductivities of transparent conducting oxides (TCO), namely aluminum-doped zinc oxide (AZO), gallium-doped zinc oxide (GZO) and tin-doped indium oxide (ITO), were investigated by terahertz time domain spectroscopy (THz-TDS) in the frequency range from 0.5 to 18 THz using air plasma techniques, supplemented by the photoconductive antenna (PCA) method. The complex conductivities were accurately calculated using a thin film extraction algorithm and analyzed in terms of the Drude conductivity model. All the measured TCOs have a scattering time below 15 fs. We find that a phonon response must be included in the description of the broadband properties of AZO and GZO for an accurate extraction of the scattering time in these materials, which is strongly influenced by the zinc oxide phonon resonance tail even in the low frequency part of the spectrum. The conductivity of AZO is found to be more thickness dependent than GZO and ITO, indicating high importance of the surface states for electron dynamics in AZO. Finally, we measure the transmittance of the TCO films from 10 to 200 THz with Fourier transform infrared spectroscopy (FTIR) measurements, thus closing the gap between THz-TDS measurements (0.5-18 THz) and ellipsometry measurements (200-1000 THz). (C)2015 Optical Society of Americ

Effects of Starvation on Lipid Metabolism and Gluconeogenesis in Yak

This research was conducted to investigate the physiological consequences of undernourished yak. Twelve Maiwa yak (110.3±5.85 kg) were randomly divided into two groups (baseline and starvation group). The yak of baseline group were slaughtered at day 0, while the other group of yak were kept in shed without feed but allowed free access to water, salt and free movement for 9 days. Blood samples of the starvation group were collected on day 0, 1, 2, 3, 5, 7, 9 and the starved yak were slaughtered after the final blood sample collection. The liver and muscle glycogen of the starvation group decreased (p<0.01), and the lipid content also decreased while the content of moisture and ash increased (p<0.05) both in Longissimus dorsi and liver compared with the baseline group. The plasma insulin and glucose of the starved yak decreased at first and then kept stable but at a relatively lower level during the following days (p<0.01). On the contrary, the non-esterified fatty acids was increased (p<0.01). Beyond our expectation, the ketone bodies of β-hydroxybutyric acid and acetoacetic acid decreased with prolonged starvation (p<0.01). Furthermore, the mRNA expression of lipogenetic enzyme fatty acid synthase and lipoprotein lipase in subcutaneous adipose tissue of starved yak were down-regulated (p<0.01), whereas the mRNA expression of lipolytic enzyme carnitine palmitoyltransferase-1 and hormone sensitive lipase were up-regulated (p<0.01) after 9 days of starvation. The phosphoenolpyruvate carboxykinase and pyruvate carboxylase, responsible for hepatic gluconeogenesis were up-regulated (p<0.01). It was concluded that yak derive energy by gluconeogenesis promotion and fat storage mobilization during starvation but without ketone body accumulation in the plasma

CNN-based automatic segmentations and radiomics feature reliability on contrast-enhanced ultrasound images for renal tumors

ObjectiveTo investigate the feasibility and efficiency of automatic segmentation of contrast-enhanced ultrasound (CEUS) images in renal tumors by convolutional neural network (CNN) based models and their further application in radiomic analysis.Materials and methodsFrom 94 pathologically confirmed renal tumor cases, 3355 CEUS images were extracted and randomly divided into training set (3020 images) and test set (335 images). According to the histological subtypes of renal cell carcinoma, the test set was further split into clear cell renal cell carcinoma (ccRCC) set (225 images), renal angiomyolipoma (AML) set (77 images) and set of other subtypes (33 images). Manual segmentation was the gold standard and serves as ground truth. Seven CNN-based models including DeepLabV3+, UNet, UNet++, UNet3+, SegNet, MultilResUNet and Attention UNet were used for automatic segmentation. Python 3.7.0 and Pyradiomics package 3.0.1 were used for radiomic feature extraction. Performance of all approaches was evaluated by the metrics of mean intersection over union (mIOU), dice similarity coefficient (DSC), precision, and recall. Reliability and reproducibility of radiomics features were evaluated by the Pearson coefficient and the intraclass correlation coefficient (ICC).ResultsAll seven CNN-based models achieved good performance with the mIOU, DSC, precision and recall ranging between 81.97%-93.04%, 78.67%-92.70%, 93.92%-97.56%, and 85.29%-95.17%, respectively. The average Pearson coefficients ranged from 0.81 to 0.95, and the average ICCs ranged from 0.77 to 0.92. The UNet++ model showed the best performance with the mIOU, DSC, precision and recall of 93.04%, 92.70%, 97.43% and 95.17%, respectively. For ccRCC, AML and other subtypes, the reliability and reproducibility of radiomic analysis derived from automatically segmented CEUS images were excellent, with the average Pearson coefficients of 0.95, 0.96 and 0.96, and the average ICCs for different subtypes were 0.91, 0.93 and 0.94, respectively.ConclusionThis retrospective single-center study showed that the CNN-based models had good performance on automatic segmentation of CEUS images for renal tumors, especially the UNet++ model. The radiomics features extracted from automatically segmented CEUS images were feasible and reliable, and further validation by multi-center research is necessary