Vertical full-colour micro-LEDs via 2D materials-based layer transfer.

Micro-LEDs (µLEDs) have been explored for augmented and virtual reality display applications that require extremely high pixels per inch and luminance1,2. However, conventional manufacturing processes based on the lateral assembly of red, green and blue (RGB) µLEDs have limitations in enhancing pixel density3-6. Recent demonstrations of vertical µLED displays have attempted to address this issue by stacking freestanding RGB LED membranes and fabricating top-down7-14, but minimization of the lateral dimensions of stacked µLEDs has been difficult. Here we report full-colour, vertically stacked µLEDs that achieve, to our knowledge, the highest array density (5,100 pixels per inch) and the smallest size (4 µm) reported to date. This is enabled by a two-dimensional materials-based layer transfer technique15-18 that allows the growth of RGB LEDs of near-submicron thickness on two-dimensional material-coated substrates via remote or van der Waals epitaxy, mechanical release and stacking of LEDs, followed by top-down fabrication. The smallest-ever stack height of around 9 µm is the key enabler for record high µLED array density. We also demonstrate vertical integration of blue µLEDs with silicon membrane transistors for active matrix operation. These results establish routes to creating full-colour µLED displays for augmented and virtual reality, while also offering a generalizable platform for broader classes of three-dimensional integrated devices.

Hydrogen Confinement in Hexagonal Boron Nitride Bubbles Using UV Laser Illumination.

Hexagonal boron nitride (h-BN) bubbles are of significant interest to micro-scale hydrogen storage thanks to their ability to confine hydrogen gas molecules. Previous reports of h-BN bubble creation from grown h-BN films require electron beams under vacuum, making integrating with other experimental setups for hydrogen production impractical. Therefore, in this study, the formation of h-BN bubbles is demonstrated in a 20 nm h-BN film grown on a sapphire substrate with a 213 nm UV laser beam. Using atomic force microscopy, it is shown that longer illumination time induces larger h-BN bubbles up to 20 µm with higher density. It is also demonstrated that h-BN bubbles do not collapse for more than 6 months after their creation. The internal pressure and gravimetric storage capacity of h-BN bubbles are reported. A maximum internal pressure of 41 MPa and a gravimetric storage capacity of 6% are obtained. These findings show that h-BN bubbles can be a promising system for long-term hydrogen storage.

Monolithic 3D integration of 2D materials-based electronics towards ultimate edge computing solutions.

Three-dimensional (3D) hetero-integration technology is poised to revolutionize the field of electronics by stacking functional layers vertically, thereby creating novel 3D circuity architectures with high integration density and unparalleled multifunctionality. However, the conventional 3D integration technique involves complex wafer processing and intricate interlayer wiring. Here we demonstrate monolithic 3D integration of two-dimensional, material-based artificial intelligence (AI)-processing hardware with ultimate integrability and multifunctionality. A total of six layers of transistor and memristor arrays were vertically integrated into a 3D nanosystem to perform AI tasks, by peeling and stacking of AI processing layers made from bottom-up synthesized two-dimensional materials. This fully monolithic-3D-integrated AI system substantially reduces processing time, voltage drops, latency and footprint due to its densely packed AI processing layers with dense interlayer connectivity. The successful demonstration of this monolithic-3D-integrated AI system will not only provide a material-level solution for hetero-integration of electronics, but also pave the way for unprecedented multifunctional computing hardware with ultimate parallelism.

Psychometric validation of the reconstructed version of the assessment of reasoning tool.

BACKGROUND: Assessing learners' competence in diagnostic reasoning is challenging and unstandardized in medical education. We developed a theory-informed, behaviorally anchored rubric, the Assessment of Reasoning Tool (ART), with content and response process validity. This study gathered evidence to support the internal structure and the interpretation of measurements derived from this tool. METHODS: We derived a reconstructed version of ART (ART-R) as a 15-item, 5-point Likert scale using the ART domains and descriptors. A psychometric evaluation was performed. We created 18 video variations of learner oral presentations, portraying different performance levels of the ART-R. RESULTS: 152 faculty viewed two videos and rated the learner globally and then using the ART-R. The confirmatory factor analysis showed a favorable comparative fit index = 0.99, root mean square error of approximation = 0.097, and standardized root mean square residual = 0.026. The five domains, hypothesis-directed information gathering, problem representation, prioritized differential diagnosis, diagnostic evaluation, and awareness of cognitive tendencies/emotional factors, had high internal consistency. The total score for each domain had a positive association with the global assessment of diagnostic reasoning. CONCLUSIONS: Our findings provide validity evidence for the ART-R as an assessment tool with five theoretical domains, internal consistency, and association with global assessment.

Identification of lateralized compensatory neural activities within the social brain due to autism spectrum disorder in adolescent males.

Socio-behavioral impairments are important characteristics of autism spectrum disorders (ASD) and MRI-based studies are pursued to identify a neurobiological basis behind these conditions. This paper presents an MRI-based study undertaken to (i) identify the differences in brain activities due to ASD, (ii) verify whether such differences exist within the 'social brain' circuit which is hypothesized to be responsible for social functions, and (iii) uncover potential compensatory mechanisms within the identified differences in brain activities. In this study, a whole-brain voxel-wise analysis is performed using resting-state fMRI data from 598 adolescent males, that is openly available from the ABIDE consortium. A new method is developed, which can (i) extract the discriminative brain activities, that provide high separability between the blood oxygenation time-series signals from ASD and neurotypical populations, (ii) select the activities that are relevant to ASD by evaluating the correlation between the separability and traditional severity scores, and (iii) map the spatial pattern of regions responsible for generating the discriminative activities. The results show that the most discriminative brain activities occur within a subset of the social brain that is involved with affective aspects of social processing, thereby supporting the idea of the social brain and also its fractionalization in ASD. Further, it has also been found that the diminished activities in the posterior cingulate area are potentially compensated by enhanced activities in the ventromedial prefrontal and anterior temporal areas within the social brain. Hemispherical lateralization is also observed on such compensatory activities.

Nanoselective area growth of defect-free thick indium-rich InGaN nanostructures on sacrificial ZnO templates.

Nanoselective area growth (NSAG) by metal organic vapor phase epitaxy of high-quality InGaN nanopyramids on GaN-coated ZnO/c-sapphire is reported. Nanopyramids grown on epitaxial low-temperature GaN-on-ZnO are uniform and appear to be single crystalline, as well as free of dislocations and V-pits. They are also indium-rich (with homogeneous 22% indium incorporation) and relatively thick (100 nm). These properties make them comparable to nanostructures grown on GaN and AlN/Si templates, in terms of crystallinity, quality, morphology, chemical composition and thickness. Moreover, the ability to selectively etch away the ZnO allows for the potential lift-off and transfer of the InGaN/GaN nanopyramids onto alternative substrates, e.g. cheaper and/or flexible. This technology offers an attractive alternative to NSAG on AlN/Si as a platform for the fabrication of high quality, thick and indium-rich InGaN monocrystals suitable for cheap, flexible and tunable light-emitting diodes.

Protein secondary structure prediction using a small training set (compact model) combined with a Complex-valued neural network approach.

BACKGROUND: Protein secondary structure prediction (SSP) has been an area of intense research interest. Despite advances in recent methods conducted on large datasets, the estimated upper limit accuracy is yet to be reached. Since the predictions of SSP methods are applied as input to higher-level structure prediction pipelines, even small errors may have large perturbations in final models. Previous works relied on cross validation as an estimate of classifier accuracy. However, training on large numbers of protein chains compromises the classifier ability to generalize to new sequences. This prompts a novel approach to training and an investigation into the possible structural factors that lead to poor predictions. Here, a small group of 55 proteins termed the compact model is selected from the CB513 dataset using a heuristics-based approach. In a prior work, all sequences were represented as probability matrices of residues adopting each of Helix, Sheet and Coil states, based on energy calculations using the C-Alpha, C-Beta, Side-chain (CABS) algorithm. The functional relationship between the conformational energies computed with CABS force-field and residue states is approximated using a classifier termed the Fully Complex-valued Relaxation Network (FCRN). The FCRN is trained with the compact model proteins. RESULTS: The performance of the compact model is compared with traditional cross-validated accuracies and blind-tested on a dataset of G Switch proteins, obtaining accuracies of ∼81 %. The model demonstrates better results when compared to several techniques in the literature. A comparative case study of the worst performing chain identifies hydrogen bond contacts that lead to Coil ⇔ Sheet misclassifications. Overall, mispredicted Coil residues have a higher propensity to participate in backbone hydrogen bonding than correctly predicted Coils. CONCLUSIONS: The implications of these findings are: (i) the choice of training proteins is important in preserving the generalization of a classifier to predict new sequences accurately and (ii) SSP techniques sensitive in distinguishing between backbone hydrogen bonding and side-chain or water-mediated hydrogen bonding might be needed in the reduction of Coil ⇔ Sheet misclassifications.

Investigation of the Performance of HEMT-Based NO, NO2 and NH3 Exhaust Gas Sensors for Automotive Antipollution Systems.

We report improved sensitivity to NO, NO2 and NH3 gas with specially-designed AlGaN/GaN high electron mobility transistors (HEMT) that are suitable for operation in the harsh environment of diesel exhaust systems. The gate of the HEMT device is functionalized using a Pt catalyst for gas detection. We found that the performance of the sensors is enhanced at a temperature of 600 °C, and the measured sensitivity to 900 ppm-NO, 900 ppm-NO2 and 15 ppm-NH3 is 24%, 38.5% and 33%, respectively, at 600 °C. We also report dynamic response times as fast as 1 s for these three gases. Together, these results indicate that HEMT sensors could be used in a harsh environment with the ability to control an anti-pollution system in real time.

Breast self-examination practices among young rural women and its associated knowledge and attitudes in Tirunelveli District, Tamil Nadu.

CONTEXT: Breast self-examination (BSE) is a simple and cost-effective screening procedure in downstaging breast tumors. AIM: To assess the BSE practices and its associated knowledge and attitudes of rural women from Tirunelveli District, Tamil Nadu during the COVID-19 pandemic. SETTINGS AND DESIGN: A descriptive cross-sectional survey design was employed, and snowball sampling was used to recruit the sample of rural women from Tirunelveli. MATERIALS AND METHODS: Women ages 18-60 willing to take part were included. Data were collected online through a self-developed questionnaire. STATISTICAL ANALYSIS: Responses were analyzed using SPSS Version 20. Descriptive statistical analysis was used to present the general details and responses of the rural women through percentages. Using the appropriate tests, the mean differences of the BSE attitudes based on the personal variables were computed using one-way ANOVA. RESULTS: A total of 433 rural women (Mean age: 29.20 ± 9.35 years) from Tirunelveli responded to the online Knowledge, Attitude and Practice (KAP) questionnaire. Regular health checkups were undergone by 27.48% of women and 9.24% underwent breast cancer (BC) screening in the past. While 68.36% had heard of BC, 61% knew it could be detected in the early stages. Insufficient knowledge regarding BSE techniques was evident among the women. Knowledge about BC was highest among those earning more than 20,001 INR, women aged 36-45, widowed/separated/divorced women, and diploma graduates. Overall, BSE and BC knowledge score was low, with correspondingly low attitudes and practices. CONCLUSION: Findings showed the KAP among rural Tirunelveli women to be low.

High energy density in artificial heterostructures through relaxation time modulation.

Electrostatic capacitors are foundational components of advanced electronics and high-power electrical systems owing to their ultrafast charging-discharging capability. Ferroelectric materials offer high maximum polarization, but high remnant polarization has hindered their effective deployment in energy storage applications. Previous methodologies have encountered problems because of the deteriorated crystallinity of the ferroelectric materials. We introduce an approach to control the relaxation time using two-dimensional (2D) materials while minimizing energy loss by using 2D/3D/2D heterostructures and preserving the crystallinity of ferroelectric 3D materials. Using this approach, we were able to achieve an energy density of 191.7 joules per cubic centimeter with an efficiency greater than 90%. This precise control over relaxation time holds promise for a wide array of applications and has the potential to accelerate the development of highly efficient energy storage systems.

Empirical Study of Protein Feature Representation on Deep Belief Networks Trained With Small Data for Secondary Structure Prediction.

Protein secondary structure (SS) prediction is a classic problem of computational biology and is widely used in structural characterization and to infer homology. While most SS predictors have been trained on thousands of sequences, a previous approach had developed a compact model of training proteins that used a C-Alpha, C-Beta Side Chain (CABS)-algorithm derived energy based feature representation. Here, the previous approach is extended to Deep Belief Networks (DBN). Deep learning methods are notorious for requiring large datasets and there is a wide consensus that training deep models from scratch on small datasets, works poorly. By contrast, we demonstrate a simple DBN architecture containing a single hidden layer, trained only on the CB513 dataset. Testing on an independent set of G Switch proteins improved the Q 3 score of the previous compact model by almost 3%. The findings are further confirmed by comparison to several deep learning models which are trained on thousands of proteins. Finally, the DBN performance is also compared with Position Specific Scoring Matrix (PSSM)-profile based feature representation. The importance of (i) structural information in protein feature representation and (ii) complementary small dataset learning approaches for detection of structural fold switching are demonstrated.

(Al, Ga)N-Based Quantum Dots Heterostructures on h-BN for UV-C Emission.

Aluminium Gallium Nitride (AlyGa1-yN) quantum dots (QDs) with thin sub-µm AlxGa1-xN layers (with x > y) were grown by molecular beam epitaxy on 3 nm and 6 nm thick hexagonal boron nitride (h-BN) initially deposited on c-sapphire substrates. An AlN layer was grown on h-BN and the surface roughness was investigated by atomic force microscopy for different deposited thicknesses. It was shown that for thicker AlN layers (i.e., 200 nm), the surface roughness can be reduced and hence a better surface morphology is obtained. Next, AlyGa1-yN QDs embedded in Al0.7Ga0.3N cladding layers were grown on the AlN and investigated by atomic force microscopy. Furthermore, X-ray diffraction measurements were conducted to assess the crystalline quality of the AlGaN/AlN layers and examine the impact of h-BN on the subsequent layers. Next, the QDs emission properties were studied by photoluminescence and an emission in the deep ultra-violet, i.e., in the 275-280 nm range was obtained at room temperature. Finally, temperature-dependent photoluminescence was performed. A limited decrease in the emission intensity of the QDs with increasing temperatures was observed as a result of the three-dimensional confinement of carriers in the QDs.

Tf-GCZSL: Task-free generalized continual zero-shot learning.

Learning continually from a stream of training data or tasks with an ability to learn the unseen classes using a zero-shot learning framework is gaining attention in the literature. It is referred to as continual zero-shot learning (CZSL). Existing CZSL requires clear task-boundary information during training which is not practically feasible. This paper proposes a task-free generalized CZSL (Tf-GCZSL) method with short-term/long-term memory to overcome the requirement of task-boundary in training. A variational autoencoder (VAE) handles the fundamental ZSL tasks. The short-term and long-term memory help to overcome the condition of the task boundary in the CZSL framework. Further, the proposed Tf-GCZSL method combines the concept of experience replay with dark knowledge distillation and regularization to overcome the catastrophic forgetting issues in a continual learning framework. Finally, the Tf-GCZSL uses a fully connected classifier developed using the synthetic features generated at the latent space of the VAE. The performance of the proposed Tf-GCZSL is evaluated in the existing task-agnostic prediction setting and the proposed task-free setting for the generalized CZSL over the five ZSL benchmark datasets. The results clearly indicate that the proposed Tf-GCZSL improves the prediction at least by 12%, 1%, 3%, 4%, and 3% over existing state-of-the-art and baseline methods for CUB, aPY, AWA1, AWA2, and SUN datasets, respectively in both settings (task-agnostic prediction and task-free learning). The source code is available at https://github.com/Chandan-IITI/Tf-GCZSL.

Crystalline Quality and Surface Morphology Improvement of Face-to-Face Annealed MBE-Grown AlN on h-BN.

In this study, AlN epilayers were grown by ammonia-assisted molecular beam epitaxy on 3 nm h-BN grown on c-sapphire substrates. Their structural properties were investigated by comparing as-grown and postgrowth annealed layers. The role of annealing on the crystalline quality and surface morphology was studied as a function of AlN thickness and the annealing duration and temperature. Optimum annealing conditions were identified. The results of X-ray diffraction showed that optimization of the annealing recipe led to a significant reduction in the symmetric (0 0 0 2) and skew symmetric (1 0 -1 1) reflections, which was associated with a reduction in edge and mixed threading dislocation densities (TDDs). Furthermore, the impact on the crystalline structure of AlN and its surface was studied, and the results showed a transition from a surface with high roughness to a smoother surface morphology with a significant reduction in roughness. In addition, the annealing duration was increased at 1650 °C to further understand the impact on both AlN and h-BN, and the results showed a diffusion interplay between AlN and h-BN. Finally, an AlN layer was regrown on the top of an annealed template, which led to large terraces with atomic steps and low roughness.

Natural Boron and 10B-Enriched Hexagonal Boron Nitride for High-Sensitivity Self-Biased Metal-Semiconductor-Metal Neutron Detectors.

Metal-semiconductor-metal (MSM) detectors based on Ti/Au and Ni/Au interdigitated structures were fabricated using 2.5 micrometer thick hexagonal boron nitride (h-BN) layer with both natural and 10B-enriched boron. Current-voltage (I-V) and current-time (I-t) curves of the fabricated detectors were recorded with (I N) and without (I d) neutron irradiation, allowing the determination of their sensitivity (S = (I N - I d)/I d = ΔI/I d). Natural and 10B-enriched h-BN detectors exhibited high neutron sensitivities of 233 and 367% at 0 V bias under a flux of 3 × 104 n/cm2/s, respectively. An imbalance in the distribution of filled traps between the two electric contacts could explain the self-biased operation of the MSM detectors. Neutron sensitivity is further enhanced with electrical biasing, reaching 316 and 1192% at 200 V and a flux of 3 × 104 n/cm2/s for natural and 10B-enriched h-BN detectors, respectively, with dark current as low as 2.5 pA at 200 V. The increased performance under bias has been attributed to a gain mechanism based on neutron-induced charge carrier trapping at the semiconductor/metal interface. The response of the MSM detectors under thermal neutron flux and bias voltages was linear. These results clearly indicate that the thin-film monocrystal BN MSM neutron detectors can be optimized to operate sensitively with the absence of external bias and generate stronger signal detection using 10B-enriched boron.

Towards P-Type Conduction in Hexagonal Boron Nitride: Doping Study and Electrical Measurements Analysis of hBN/AlGaN Heterojunctions.

Reliable p-doped hexagonal boron nitride (h-BN) could enable wide bandgap optoelectronic devices such as deep ultra-violet light emitting diodes (UV LEDs), solar blind photodiodes and neutron detectors. We report the study of Mg in h-BN layers as well as Mg h-BN/AlGaN heterostructures. Mg incorporation in h-BN was studied under different biscyclopentadienyl-magnesium (Cp2Mg) molar flow rates. 2θ-ω x-ray diffraction scans clearly evidence a single peak, corresponding to the (002) reflection plane of h-BN with a full-width half maximum increasing with Mg incorporation in h-BN. For a large range of Cp2Mg molar flow rates, the surface of Mg doped h-BN layers exhibited characteristic pleats, confirming that Mg doped h-BN remains layered. Secondary ion mass spectrometry analysis showed Mg incorporation, up to 4 × 1018 /cm3 in h-BN. Electrical conductivity of Mg h-BN increased with increased Mg-doping. Heterostructures of Mg h-BN grown on n-type Al rich AlGaN (58% Al content) were made with the intent of forming a p-n heterojunction. The I-V characteristics revealed rectifying behavior for temperatures from 123 to 423 K. Under ultraviolet illumination, photocurrent was generated, as is typical for p-n diodes. C-V measurements evidence a built-in potential of 3.89 V. These encouraging results can indicate p-type behavior, opening a pathway for a new class of wide bandgap p-type layers.

Mission Aware Motion Planning (MAP) Framework With Physical and Geographical Constraints for a Swarm of Mobile Stations.

In this paper, we propose a mission aware motion planning (MAP) framework for a swarm of autonomous unmanned ground vehicles (UGVs) or mobile stations in an uncertain environment for efficient supply of resources/services to unmanned aerial vehicles (UAVs) performing a specific mission. The MAP framework consists of two levels, namely, centralized mission planning and decentralized motion planning. On the first level, the centralized mission planning algorithm estimates the density of UAV in a given environment for determining the number of UGVs and their initial operating location. In the subsequent level, a decentralized motion planning algorithm which provides a closed-form expression for velocity command using adaptive density estimation has been proposed. Further, the physical and geographical constraints are integrated into motion planning. A Monte-Carlo simulation is performed to evaluate the advantages of the MAP over distributed stationary stations (DSSs) often used in the literature. The obtained results clearly indicate that in comparison with DSS, MAP reduces the average distance traveled by UAVs about 20%, reduces the loss of mission time by 90 s per interruption and power loss by 3 dB.

Metaheuristic Spatial Transformation (MST) for accurate detection of Attention Deficit Hyperactivity Disorder (ADHD) using rs-fMRI.

Accurate detection of neuro-psychological disorders such as Attention Deficit Hyperactivity Disorder (ADHD) using resting state functional Magnetic Resonance Imaging (rs-fMRI) is challenging due to high dimensionality of input features, low inter-class separability, small sample size and high intra-class variability. For automatic diagnosis of ADHD and autism, spatial transformation methods have gained significance and have achieved improved classification performance. However, they are not reliable due to lack of generalization in dataset like ADHD with high variance and small sample size. Therefore, in this paper, we present a Metaheuristic Spatial Transformation (MST) approach to convert the spatial filter design problem into a constraint optimization problem, and obtain the solution using a hybrid genetic algorithm. Highly separable features obtained from the MST along with meta-cognitive radial basis function based classifier are utilized to accurately classify ADHD. The performance was evaluated using the ADHD200 consortium dataset using a ten fold cross validation. The results indicate that the MST based classifier produces state of the art classification accuracy of 72.10% (1.71% improvement over previous transformation based methods). Moreover, using MST based classifier the training and testing specificity increased significantly over previous methods in literature. These results clearly indicate that MST enables the determination of the highly discriminant transformation in dataset with high variability, small sample size and large number of features. Further, the performance on the ADHD200 dataset shows that MST based classifier can be reliably used for the accurate diagnosis of ADHD using rs-fMRI.Clinical relevance- Metaheuristic Spatial Transformation (MST) enables reliable and accurate detection of neuropsychological disorders like ADHD from rs-fMRI data characterized by high variability, small sample size and large number of features.

Control of the Mechanical Adhesion of III-V Materials Grown on Layered h-BN.

Hexagonal boron nitride (h-BN) can be used as a p-doped material in wide-bandgap optoelectronic heterostructures or as a release layer to allow lift-off of grown three-dimensional (3D) GaN-based devices. To date, there have been no studies of factors that lead to or prevent lift-off and/or spontaneous delamination of layers. Here, we report a unique approach of controlling the adhesion of this layered material, which can result in both desired lift-off layered h-BN and mechanically inseparable robust h-BN layers. This is accomplished by controlling the diffusion of Al atoms into h-BN from AlN buffers grown on h-BN/sapphire. We present evidence of Al diffusion into h-BN for AlN buffers grown at high temperatures compared to conventional-temperature AlN buffers. Further evidence that the Al content in BN controls lift-off is provided by comparison of two alloys, Al0.03B0.97N/sapphire and Al0.17B0.83N/sapphire. Moreover, we tested that management of Al diffusion controls the mechanical adhesion of high-electron-mobility transistor (HEMT) devices grown on AlN/h-BN/sapphire. The results extend the control of two-dimensional (2D)/3D hetero-epitaxy and bring h-BN closer to industrial application in optoelectronics.

Effectiveness of selective area growth using van der Waals h-BN layer for crack-free transfer of large-size III-N devices onto arbitrary substrates.

Selective Area van der Waals Epitaxy (SAVWE) of III-Nitride device has been proposed recently by our group as an enabling solution for h-BN-based device transfer. By using a patterned dielectric mask with openings slightly larger than device sizes, pick-and-place of discrete LEDs onto flexible substrates was achieved. A more detailed study is needed to understand the effect of this selective area growth on material quality, device performance and device transfer. Here we present a study performed on two types of LEDs (those grown on h-BN on patterned and unpatterned sapphire) from the epitaxial growth to device performance and thermal dissipation measurements before and after transfer. Millimeter-size LEDs were transferred to aluminum tape and to silicon substrates by van der Waals liquid capillary bonding. It is shown that patterned samples lead to a better material quality as well as improved electrical and optical device performances. In addition, patterned structures allowed for a much better transfer yield to silicon substrates than unpatterned structures. We demonstrate that SAVWE, combined with either transfer processes to soft or rigid substrates, offers an efficient, robust and low-cost heterogenous integration capability of large-size devices to silicon for photonic and electronic applications.