bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tri-halide vapor-phase epitaxy of GaN using GaCl3 on polar, semipolar, and nonpolar substrates
Authors: Iso Kenji , Takekawa Nao , Matsuda Karen , Hikida Kazuhiro , Hayashida Naoto , Murakami Hisashi , Koukitu Akinori 
Published: September 2016

Abstract
Homoepitaxial tri-halide vapor-phase epitaxy (THVPE) growth on polar, semipolar, and nonpolar bulk GaN substrates was demonstrated using GaCl3 as the precursor. The influence of the surface orientation of the substrate on GaN growth by THVPE was compared with that observed for GaN grown by hydride vapor-phase epitaxy. The dependence of the GaN growth on the surface orientation of the substrate was confirmed; GaN could be grown on

,

,

,

, and

but not on

,

,

, or

. This behavior was explained to be due to the changes in adsorption energy, the magnitudes of which were estimated by theoretical calculations.
Integrated optoelectronic model for organic solar cells based on the finite element method including the effect of oblique sunlight incidence and a non-ohmic electrode contact
Authors: Lee Yongjeong , Kang Kyungnam , Lee Sanghwa , Kim Hyeong Pil , Jang Jin , Kim Jungho 
Published: September 2016

Abstract
We present an integrated optoelectronic model for organic solar cells (OSCs) based on the finite element method, which can numerically simulate the optical and electrical properties in the same calculation domain. In the optical model, the spatial distribution of optical absorption is calculated with respect to the incidence angle and light polarization. A glass factor is introduced to include the incoherent light interaction inside the thick glass substrate. In the electrical model, the current density–voltage (J–V) characteristics can be calculated by self-consistently solving the combined equations based on the Onsager–Braun charge-transfer exciton dissociation, drift-diffusion carrier transport, and non-ohmic contact models. The calculation results of the carrier density, the electric potential, and the electric field in the active layer are compared between the ohmic and non-ohmic contact models at the electrode–organic interface. We numerically calculate the angular and polarization dependences of the short-circuit current density, the open-circuit voltage, and the output electric power density at the spectral irradiance of the AM 1.5 spectrum. The calculation results are well matched with the experimental results at various incidence angles and light polarizations. The application of the proposed integrated optoelectronic model to OSCs will not be restricted to one-dimensional planar structures and can be extended to nonplanar or surface-textured structures.
Analytic modeling of potential and threshold voltage for short-channel thin-body fully depleted silicon-on-insulator MOSFETs with a vertical Gaussian doping profile
Authors: Wei Sufen , Zhang Guohe , Shao Zhibiao , Huang Huixiang , Geng Li 
Published: September 2016

Abstract
We verify that one-dimensional (1D) Gaussian expression is an appropriate approximation of the vertical doping profile, which is obtained by combining perpendicular ion implantation and rapid thermal annealing (RTA), for short-channel thin-body (20–30 nm) fully depleted (FD) silicon-on-insulator (SOI) MOSFETs. The two-dimensional (2D) potential distribution of the silicon film is derived by adopting the evanescent mode analysis method, in which the potential function is broken into 1D long-channel and 2D short-channel potentials. The threshold voltage model is represented by the minimum front- and back-surface potentials of the silicon film. The application of the threshold voltage model can be extended to a 12 nm channel length. The results obtained using the models match well with the 2D numerical simulation results obtained using the Synopsys Sentaurus Device™. They provide a feasible way of developing new 2D models for nonuniform nanoscale thin-body FD-SOI devices.
Automated confidence estimation in deep learning auto‐segmentation for brain organs at risk on MRI for radiotherapy
Authors: Alzahrani Nouf M. , Henry Ann M. , Al‐Qaisieh Bashar M. , Murray Louise J. , Nix Michael G. 
Published: September 2024

AbstractPurposeWe have built a novel AI‐driven QA method called AutoConfidence (ACo), to estimate segmentation confidence on a per‐voxel basis without gold standard segmentations, enabling robust, efficient review of automated segmentation (AS). We have demonstrated this method in brain OAR AS on MRI, using internal and external (third‐party) AS models.MethodsThirty‐two retrospectives, MRI planned, glioma cases were randomly selected from a local clinical cohort for ACo training. A generator was trained adversarialy to produce internal autosegmentations (IAS) with a discriminator to estimate voxel‐wise IAS uncertainty, given the input MRI. Confidence maps for each proposed segmentation were produced for operator use in AS editing and were compared with “difference to gold‐standard” error maps. Nine cases were used for testing ACo performance on IAS and validation with two external deep learning segmentation model predictions [external model with low‐quality AS (EM‐LQ) and external model with high‐quality AS (EM‐HQ)]. Matthew's correlation coefficient (MCC), false‐positive rate (FPR), false‐negative rate (FNR), and visual assessment were used for evaluation. Edge removal and geometric distance corrections were applied to achieve more useful and clinically relevant confidence maps and performance metrics.ResultsACo showed generally excellent performance on both internal and external segmentations, across all OARs (except lenses). MCC was higher on IAS and low‐quality external segmentations (EM‐LQ) than high‐quality ones (EM‐HQ). On IAS and EM‐LQ, average MCC (excluding lenses) varied from 0.6 to 0.9, while average FPR and FNR were ≤0.13 and ≤0.21, respectively. For EM‐HQ, average MCC varied from 0.4 to 0.8, while average FPR and FNR were ≤0.37 and ≤0.22, respectively.ConclusionACo was a reliable predictor of uncertainty and errors on AS generated both internally and externally, demonstrating its potential as an independent, reference‐free QA tool, which could help operators deliver robust, efficient autosegmentation in the radiotherapy clinic.
Evaluation of a Metal Artifact Reduction Algorithm for Image Reconstruction on a Novel CBCT Platform
Authors: Yashayaeva Abby , MacDonald Robert Lee , Robar James , Cherpak Amanda 
Published: September 2024

AbstractPurposeThe presence of metal implants can produce artifacts and distort Hounsfield units (HU) in patient computed tomography (CT) images. The purpose of this work was to characterize a novel metal artifact reduction (MAR) algorithm for reconstruction of CBCT images obtained by the HyperSight imaging system.MethodsThree tissue‐equivalent phantoms were fitted with materials commonly used in medical applications. The first consisted of a variety of metal samples centered within a solid water block, the second was an Advanced Electron Density phantom with metal rods, and the third consisted of hip prostheses positioned within a water tank. CBCT images of all phantoms were acquired and reconstructed using the MAR and iCBCT Acuros algorithms on the HyperSight system. The signal‐to‐noise ratio (SNR), artifact index (AI), structural similarity index measure (SSIM), peak signal‐to‐noise ratio (PSNR), and mean‐square error (MSE) were computed to assess the image quality in comparison to artifact‐free reference images. The mean HU at various VOI positions around the cavity was calculated to evaluate the artifact dependence on distance and angle from the center of the cavity. The artifact volume of the phantom (excluding the cavity) was estimated by summing the volume of all voxels with HU values outside the 5th and 95th percentiles of the phantom CBCT with no artifact.ResultsThe SNR, AI, SSIM, PSNR, and MSE metrics demonstrated significantly higher similarity to baseline when using MAR compared to iCBCT Acuros for all high‐density materials, except for aluminum. Mean HU returned to expected solid water background at a shorter distance from metal sample in the MAR images, and the standard deviation remained lower for the MAR images at all distances from the insert. The artifact volume decreased using the novel MAR algorithm for all metal samples excluding aluminum (p < 0.001) and all hip prostheses (p < 0.05).ConclusionVarian's HyperSight MAR reconstruction algorithm shows a reduction in metal artifact metrics, motivating the use of MAR reconstruction for patients with metal implants.
Evaluation of deep learning based dose prediction in head and neck cancer patients using two different types of input contours
Authors: Saito Masahide , Kadoya Noriyuki , Kimura Yuto , Nemoto Hikaru , Tozuka Ryota , Jingu Keiichi , Onishi Hiroshi 
Published: September 2024

AbstractPurposeThis study evaluates deep learning (DL) based dose prediction methods in head and neck cancer (HNC) patients using two types of input contours.Materials and methodsSeventy‐five HNC patients undergoing two‐step volumetric‐modulated arc therapy were included. Dose prediction was performed using the AIVOT prototype (AiRato.Inc, Sendai, Japan), a commercial software with an HD U‐net‐based dose distribution prediction system. Models were developed for the initial plan (46 Gy/23Fr) and boost plan (24 Gy/12Fr), trained with 65 cases and tested with 10 cases. The 8‐channel model used one target (PTV) and seven organs at risk (OARs), while the 10‐channel model added two dummy contours (PTV ring and spinal cord PRV). Predicted and deliverable doses, obtained through dose mimicking on another radiation treatment planning system, were evaluated using dose‐volume indices for PTV and OARs.ResultsFor the initial plan, both models achieved approximately 2% prediction accuracy for the target dose and maintained accuracy within 3.2 Gy for OARs. The 10‐channel model outperformed the 8‐channel model for certain dose indices. For the boost plan, both models exhibited prediction accuracies of approximately 2% for the target dose and 1 Gy for OARs. The 10‐channel model showed significantly closer predictions to the ground truth for D50% and Dmean. Deliverable plans based on prediction doses showed little significant difference compared to the ground truth, especially for the boost plan.ConclusionDL‐based dose prediction using the AIVOT prototype software in HNC patients yielded promising results. While additional contours may enhance prediction accuracy, their impact on dose mimicking is relatively small.
Comparative assessment and QA measurement array validation of Monte Carlo and Collapsed Cone dose algorithms for small fields and clinical treatment plans
Authors: Spenkelink Guus B. , Huijskens Sophie C. , Zindler Jaap D. , de Goede Marc , van der Star Wilhelmus J. , van Egmond Jaap , Petoukhova Anna L. 
Published: September 2024

AbstractPurposeMany studies have demonstrated superior performance of Monte Carlo (MC) over type B algorithms in heterogeneous structures. However, even in homogeneous media, MC dose simulations should outperform type B algorithms in situations of electronic disequilibrium, such as small and highly modulated fields.Our study compares MC and Collapsed Cone (CC) dose algorithms in RayStation 12A. Under consideration are 6 MV and 6 MV flattening filter‐free (FFF) photon beams, relevant for VMAT plans such as head‐and‐neck and stereotactic lung treatments with heterogeneities, as well as plans for multiple brain metastases in one isocenter, involving highly modulated small fields. We aim to investigate collimator angle dependence of small fields and performance differences between different combinations of ArcCHECK configuration and dose algorithm.MethodsSeveral verification tests were performed, ranging from simple rectangular fields to highly modulated clinical plans. To evaluate and compare the performance of the models, the agreements between calculation and measurement are compared between MC and CC. Measurements include water tank measurements for test fields, ArcCHECK measurements for test fields and VMAT plans, and film dosimetry for small fields.Results and conclusionsIn very small or narrow fields, our measurements reveal a strong dependency of dose output to collimator angle for VersaHD with Agility MLC, reproduced by both dose algorithms.ArcCHECK results highlight a suboptimal agreement between measurements and MC calculations for simple rectangular fields when using inhomogeneous ArcCHECK images. Therefore, we advocate for the use of homogeneous phantom images, particularly for static fields, in ArcCHECK verification with MC.MC might offer performance benefits for more modulated treatment fields. In ArcCHECK results for clinical plans, MC performed comparable to CC for 6 MV. For 6 MV FFF and the preferred homogeneous phantom image, MC resulted in consistently better results (13%–64% lower mean gamma index) compared to CC.
Brain‐inspired Multimodal Synaptic Memory via Mechano‐photonic Plasticized Asymmetric Ferroelectric Heterostructure
Authors: Gong Jie , Wei Yichen , Wang Yifei , Feng Zhenyu , Yu Jinran , Cheng Liuqi , Chen Mingxia , Li Linlin , Wang Zhong Lin , Sun Qijun 
Published: September 2024

AbstractNeuromorphic devices capable of emulating biological synaptic behaviors are crucial for implementing brain‐like information processing and computing. Emerging 2D ferroelectric neuromorphic devices provide an effective means of updating synaptic weight aside from conventional electrical/optical modulations. Here, by further synergizing with an energy‐efficient synaptic plasticity strategy, a multimodal mechano‐photonic synaptic memory device based on 2D asymmetric ferroelectric heterostructure is presented, which can be modulated by external mechanical behavior and light illumination. By integrating the asymmetric ferroelectric heterostructured field‐effect transistor and a triboelectric nanogenerator, the mechanical displacement‐derived triboelectric potential is ready for gating, programming, and plasticizing the synaptic device, resulting in superior electrical properties of high on/off ratios (> 107), large storage windows (equivalent to ≈95 V), excellent charge retention capability (> 104 s), good endurance (> 103 cycles), and primary synaptic behaviors. Besides, optical illumination can effectively synergize with mechanoplasticity to implement multimodal spatiotemporally correlated dynamic logic. The demonstrated multimodal memory synapse provides a facile and promising strategy for multifunctional sensory memory, interactive neuromorphic devices, and future brain‐like electronics embodying artificial intelligence.
MXenes: Multifunctional Materials for the Smart Cities of Tomorrow
Authors: Purbayanto Muhammad A. K. , Presser Volker , Skarżyński Kacper , Słoma Marcin , Naguib Michael , Jastrzębska Agnieszka M. 
Published: September 2024

AbstractCurrently, over 60% of the world's population lives in cities. Urban living has many advantages but there are also challenges regarding the need for smart urbanization. The next generation of tunable 2D nanomaterials, called MXenes, is the critical enabling technology that can bring the current urban thinking to the next level, called a smart city. The smart city is a novel concept based on a framework of self‐sufficient technologies that are interactive and responsive to citizens’ needs. In this perspective, MXene‐enabled technologies for sustainable urban development are discussed. They can advance self‐sufficient, adaptive, and responsive buildings that can minimize resource consumption, solving the deficiency of essential resources such as clean energy, water, and air. MXenes are at the cutting edge of technological limitations associated with the Internet of Things (IoT) and telemedicine, combining diverse properties and offering multitasking. It is foreseen that MXenes can have a bright future in contributing to the smart city concept. Therefore, the roadmap is presented for demonstrating the practical feasibility of MXenes in the smart city. Altogether, this study promotes the role of MXenes in advancing the well‐being of citizens by raising the quality of urban living to the next level.
Metastable Liquid–Liquid Phase Separation and Aging Lead to Strong Processing Path Dependence in Mini‐Spidroin Solutions
Authors: Fedorov Dmitrii , Sammalisto Fred‐Eric , Harmat Adam L. , Ahlberg Martin , Koskela Salla , Haataja Mikko P. , Scacchi Alberto , Sammalkorpi Maria , Linder Markus B. 
Published: September 2024

AbstractRecombinant silk proteins provide a route toward sustainable and biocompatible materials. For making such materials, the assembly process from dilute protein into a functional material is central. The assembly mechanism in engineered materials is by necessity different from the natural ones—this poses challenges but also opens opportunities for scaling up and for developing novel properties. The phase behavior of a mini‐spidroin, NT‐2Rep‐CT is studied, which is a widely studied variant of recombinant silk. NT‐2Rep‐CT can be triggered to assemble by lowering the pH, but even at high pH—considered as storage conditions—it can be in various states, such as forming condensates, clusters, gels, and soluble protein. It is shown how its assembly phases evolve through both metastable and dynamically arrested states. The observed behavior of silk protein solutions is highly complex, and elements thereof from phase diagrams associated with polymers, colloidal systems, and globular proteins are found. Based on the characterization of cluster formation and structural intermediates, a minimalist phase diagram is proposed for NT‐2Rep‐CT and argues that the understanding and insight into silk assembly via its phase behavior, and especially the arrested states, is central for designing recombinant silk proteins and their processing for materials applications.
3D‐Shape Recoverable Hydrogel with Highly Efficient Water Transport for Solar Water Desalination
Authors: Fu Chao , Liu Luqi , Xu Chenchen , Tao Ping , Yang Yuequan , Lu Liquan , Zeng Zhixiang , Ren Tianhui 
Published: September 2024

AbstractPorous hydrogels have been developed to be highly efficient interfacial solar vapor generation materials for obtaining affordable freshwater supplies. However, realizing hydrogel materials with portability, adequate water supply, and durable mechanical properties remains challenging. Herein, a scalable and portable hydrogel with 3D‐shaped recovery, highly efficient water transport, and robust mechanical stability is reported, which is prepared by foaming polyvinyl alcohol (PVA) and modification of phosphotungstic acid (PTA). Due to the interconnected porous structure and highly permeable polymer network, the PVA/PTA‐PH hydrogel has good repetitive compressibility and rapid shape recovery when in contact with water. Combined with light‐absorbing nanoparticles, the PVA/PTA‐PH hydrogel presents an impressive solar evaporation rate of 3.876 kg m−2 h−1 and a photothermal conversion efficiency of ≈94% under 1‐sun irradiation. With 3D‐shape recovery and highly efficient water transport, the compressed hydrogel can be quickly unfolded without human intervention and restored to five times the original size for the working state. The good portability, excellent seawater desalination performance, and simple preparation process are anticipated to make the PVA‐/PTA‐PH hydrogel a promising material to continuously and conveniently produce pure water from seawater.
Precision Self‐Assembly of Supramolecules with Heterogeneous Derivatives
Authors: Zhang Jiawen , Xiang Li , Zhou Binglin , Zhang Yuhao , Zhang Weijie , Jiang Jinyang , Zeng Hongbo 
Published: September 2024

AbstractSupramolecular self‐assembly with well‐defined building blocks like lipids, deoxyribonucleic acid, or ligands relies on accessible molecular structures and predictable interactions. However, assembling heterogeneous, undefined blocks, such as disordered proteins, amorphous solids, and catecholic derivatives, remains challenging due to their unpredictable assembly, leading to irreversible aggregation, severe precipitation, and unreliable performance. Here, the first programmable, sustainable, and durable self‐assembly strategy of supramolecules with heterogenous is presented, derived blocks via harmonizing multiple molecular interactions. This approach achieves reversible assembly/disassembly, ≈73.7% reduced precipitation, and salt‐ and alkaline‐durability under freeze‐thaw cycles in model catecholic derivatives, functioning effectively as robust adhesive primers and hydrogel interfacial strengtheners. Moreover, through molecular force measurements and computational simulations, the first general criterion and benchmark for high precision supramolecular self‐assembly is proposed, applicable to complex derivatives and interactions: with blocks bearing multiple binding sites existing, the co‐assembling blocks should bear at least two binding sites with minimum binding strength (≈17 to ≈37 kJ mol−1) to prevent disassembly. This study paves the way and provides benchmarks for precision self‐assembly of diverse supramolecules using heterogeneous derivatives for adhesion technology, nanomaterial synthesis and bio‐inspired applications.
A Full‐Process, Fine‐Grained, and Quantitative Rehabilitation Assessment Platform Enabled by On‐Skin Sensors and Multi‐Task Gait Transformer Model
Authors: Wang Zhixin , He Xinrun , Bu Tianzhao , Pang Bo , Guo Wei , Tu Zhongyi , Zhang Zhiqiang , Xiao Xiling , Yin Zhouping , Huang Jian , Wu Hao 
Published: September 2024

AbstractRehabilitation of patients with lower limb movement disorders is a gradual process, which requires full‐process assessments to guide the implementation of rehabilitation plans. However, the current methods can only complete the assessment in one stage and lack objective and quantitative assessment strategies. Here, a full‐process, fine‐grained, and quantitative rehabilitation assessments platform (RAP) supported by on‐skin sensors and a multi‐task gait transformer (MG‐former) model for patients with lower limb movement disorders is developed. The signal quality and sensitivity of on‐skin sensor is improved by the synthesis of high‐performance triboelectric material and structure design. The MG‐former model can simultaneously perform multiple tasks including binary classification, multiclassification, and regression, corresponding to assessment of fall risk, walking ability, and rehabilitation progress, covering the whole rehabilitation cycle. The RAP can assess the walking ability of 23 hemiplegic patients, which has highly consistent results with the scores by the experienced physician. Furthermore, the MG‐former model outputs fine‐grained assessment results when performing regression task to track slight progress of patients that cannot be captured by conventional scales, facilitating adjustment of rehabilitation plans. This work provides an objective and quantitative platform, which is instructive for physicians and patients to implement effective strategy throughout the whole rehabilitation process.