We report the study of a novel linear magneto-resistance (MR) under perpendicular magnetic fields in Bi 2 Se 3 nanoribbons. Through angular dependence magneto-transport experiments, we show that this linear MR is purely due to two-dimensional (2D) transport, in agreement with the recently discovered linear MR from 2D topological surface state in bulk Bi 2 Te 3 , and the linear MR of other gapless semiconductors and graphene. We further show that the linear MR of Bi 2 Se 3 nanoribbons persists to room temperature, underscoring the potential of exploiting topological insulator nanomaterials for room temperature magneto-electronic applications.KEYWORDS: Topological insulator, Bi 2 Se 3 , nanoribbon, linear magneto-resistance, twodimensional transport Topological insulators (TI's) are quantum materials with conducting gapless surface state on the surface or edge of insulating bulk, 1-4 holding great promises in the fundamental study of topological ordering in condensed matter systems and applications in spintronic devices for the spin polarized surface state. The spin polarization and suppressed back scattering render 2D topological surface state an attractive platform for high mobility charge and spin transport devices. Recently, Bi 2 Se 3 and related materials have been proposed 5 and confirmed 6-8 as threedimensional (3D) TI's with a single Dirac cone for the surface state. Among these materials, Bi 2 Se 3 , which is a pure compound rather than an alloy like Bi x Sb 1-x , 9 owns a larger bulk band gap (0.3 eV), and is thought to be promising for room temperature applications. Although the existence of topological surface state in Bi 2 Se 3 has been established by surface sensitive techniques such as the angle-resolved photoemission spectroscopy, 6,7 extracting the transport properties of 2D surface state in 3D TI's has been plagued by the more dominating conductivity from bulk carriers. [10][11][12][13][14][15][16][17][18] With extremely high surface-to-volume ratio and thus larger surface contribution in transport, nanostructures of TI's are useful to distinguish 2D surface transport from 3D bulk transport in the heated study of topological surface state. Indeed, Aharonov-Bohm (AB) oscillations were discovered in Bi 2 Se 3 nanoribbons in the parallel magnetic field induced MR, proving the existence of a coherent surface conducting channel. 10 In this study, we explore

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Purpose: Ultrasound (US)-guided high dose rate (HDR) prostate brachytherapy requests the clinicians to place HDR needles (catheters) into the prostate gland under transrectal US (TRUS) guidance in the operating room. The quality of the subsequent radiation treatment plan is largely dictated by the needle placements, which varies upon the experience level of the clinicians and the procedure protocols. Real-time plan dose distribution, if available, could be a vital tool to provide more subjective assessment of the needle placements, hence potentially improving the radiation plan quality and the treatment outcome. However, due to low signal-to-noise ratio (SNR) in US imaging, real-time multi-needle segmentation in 3D TRUS, which is the major obstacle for real-time dose mapping, has not been realized to date. In this study, we propose a deep learning-based method that enables accurate and real-time digitization of the multiple needles in the 3D TRUS images of HDR prostate brachytherapy. Methods: A deep learning model based on the U-Net architecture was developed to segment multiple needles in the 3D TRUS images. Attention gates were considered in our model to improve the prediction on the small needle points. Furthermore, the spatial continuity of needles was encoded into our model with total variation (TV) regularization. The combined network was trained on 3D TRUS patches with the deep supervision strategy, where the binary needle annotation images were provided as ground truth. The trained network was then used to localize and segment the HDR needles for a new patient's TRUS images. We evaluated our proposed method based on the needle shaft and tip errors against manually defined ground truth and compared our method with other state-ofart methods (U-Net and deeply supervised attention U-Net). Results: Our method detected 96% needles of 339 needles from 23 HDR prostate brachytherapy patients with 0.290 AE 0.236 mm at shaft error and 0.442 AE 0.831 mm at tip error. For shaft localization, our method resulted in 96% localizations with less than 0.8 mm error (needle diameter is 1.67 mm), while for tip localization, our method resulted in 75% needles with 0 mm error and 21% needles with 2 mm error (TRUS image slice thickness is 2 mm). No significant difference is observed (P = 0.83) on tip localization between our results with the ground truth. Compared with U-Net and deeply supervised attention U-Net, the proposed method delivers a significant improvement on both shaft error and tip error (P < 0.05). Conclusions: We proposed a new segmentation method to precisely localize the tips and shafts of multiple needles in 3D TRUS images of HDR prostate brachytherapy. The 3D rendering of the needles could help clinicians to evaluate the needle placements. It paves the way for the development of real-time plan dose assessment tools that can further elevate the quality and outcome of HDR prostate brachytherapy.

We report thermal conductivity measurements of individual single crystalline Bi2Se3 nano-ribbon (NR) synthesized via the gold nanoparticle catalyzed vapor-liquid-solid mechanism. By using the four-probe third harmonic method, thermal conductivity of Bi2Se3 NRs was obtained in the temperature range of 10 K to 300 K. It is found that the measured thermal conductivities are nearly two orders of magnitude smaller than the bulk value and have a maximum thermal conductivity at temperature (around 200 K) greater than the bulk. The significant reduced thermal conductivity of NRs is attributed to enhanced phonon boundary scattering in nanostructured material.

We present the transport and capacitance measurements of 10nm wide GaAs quantum wells with hole densities around the critical point of the 2D metal-insulator transition (critical density pc down to 0.8×10 10 /cm 2 , rs∼36). For metallic hole density pc

We report the composition- and gate voltage-induced tuning of transport properties in chemically synthesized Bi2(Te1-xSex)3 nanoribbons. It is found that increasing Se concentration effectively suppresses the bulk carrier transport and induces semiconducting behavior in the temperature-dependent resistance of Bi2(Te1-xSex)3 nanoribbons when x is greater than ∼10%. In Bi2(Te1-xSex)3 nanoribbons with x ≈ 20%, gate voltage enables ambipolar modulation of resistance (or conductance) in samples with thicknesses around or larger than 100 nm, indicating significantly enhanced contribution in transport from the gapless surface states.

Two infrared (IR)-active vibrational modes, observed at 93 and 113 cm(-1) in Raman scattering, are evidence of an inversion symmetry breakdown in thin (~10 nm) nanoplates of topological insulator Bi(2)Te(3) as-grown on SiO(2). Both Raman and IR modes are preserved after typical device fabrication processes. In nanoplates transferred to another SiO(2) substrate via contact printing, however, the IR modes are absent, and the Raman spectra are similar to those from bulk samples. The differences between as-grown and transferred nanoplates may result from nanoplate-substrate interactions.

Kilovoltage cone-beam computed tomography (CBCT)-based image-guided radiation therapy (IGRT) is used for daily delivery of radiation therapy, especially for stereotactic body radiation therapy (SBRT), which imposes particularly high demands for setup accuracy. The clinical applications of CBCTs are constrained, however, by poor soft tissue contrast, image artifacts, and instability of Hounsfield unit (HU) values. Here, we propose a new deep learning-based method to generate synthetic CTs (sCT) from thoracic CBCTs. A deep-learning model which integrates histogram matching (HM) into a cycle-consistent adversarial network (Cycle-GAN) framework, called HM-Cycle-GAN, was trained to learn mapping between thoracic CBCTs and paired planning CTs. Perceptual supervision was adopted to minimize blurring of tissue interfaces. An informative maximizing loss was calculated by feeding CBCT into the HM-Cycle-GAN to evaluate the image histogram matching between the planning CTs and the sCTs. The proposed algorithm was evaluated using data from 20 SBRT patients who each received 5 fractions and therefore 5 thoracic CBCTs. To reduce the effect of anatomy mismatch, original CBCT images were pre-processed via deformable image registrations with the planning CT before being used in model training and result assessment. We used planning CTs as ground truth for the derived sCTs from the correspondent co-registered CBCTs. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC) indices were adapted as evaluation metrics of the proposed algorithm. Assessments were done using Cycle-GAN as the benchmark. The average MAE, PSNR, and NCC of the sCTs generated by our method were 66.2 HU, 30.3 dB, and 0.95, respectively, over all CBCT fractions. Superior image quality and reduced noise and artifact severity were seen using the proposed method compared to the results from the standard Cycle-GAN method. Our method could therefore improve the accuracy of IGRT and corrected CBCTs could help improve online adaptive RT by offering better contouring accuracy and dose calculation.