Two-dimensional Architecture Research Articles

Automatic segmentation of white matter hyperintensities in T2-FLAIR with AQUA: A comparative validation study against conventional methods

White matter hyperintensities (WMHs) are lesions in the white matter of the brain that are associated with cognitive decline and an increased risk of dementia. The manual segmentation of WMHs is highly time-consuming and prone to intra- and inter-variability. Therefore, automatic segmentation approaches are gaining attention as a more efficient and objective means to detect and monitor WMHs. In this study, we propose AQUA, a deep learning model designed for fully automatic segmentation of WMHs from T2-FLAIR scans, which improves upon our previous study for small lesion detection and incorporating a multicenter approach. AQUA implements a two-dimensional U-Net architecture and uses patch-based training. Additionally, the network was modified to include Bottleneck Attention Module on each convolutional block of both the encoder and decoder to enhance performance for small-sized WMH. We evaluated the performance and robustness of AQUA by comparing it with five well-known supervised and unsupervised methods for automatic segmentation of WMHs (LGA, LPA, SLS, UBO, and BIANCA). To accomplish this, we tested these six methods on the MICCAI 2017 WMH Segmentation Challenge dataset, which contains MRI images from 170 elderly participants with WMHs of presumed vascular origin, and assessed their robustness across multiple sites and scanner types. The results showed that AQUA achieved superior performance in terms of spatial (Dice = 0.72) and volumetric (logAVD = 0.10) agreement with the manual segmentation compared to the other methods. While the recall and F1-score were moderate at 0.49 and 0.59, respectively, they improved to 0.75 and 0.82 when excluding small lesions (≤ 6 voxels). Remarkably, despite being trained on a different dataset with different ethnic backgrounds, lesion loads, and scanners, AQUA's results were comparable to the top 10 ranked methods of the MICCAI challenge. The findings suggest that AQUA is effective and practical for automatic segmentation of WMHs from T2-FLAIR scans, which could help identify individuals at risk of cognitive decline and dementia and allow for early intervention and management.

A Novel LSTM-based Data Synthesis Approach for Performance Improvement in Detecting Epileptic Seizures

Bio-electrical time signals play a significant role in assisting non-invasive observational procedures in healthcare. These bioelectrical signals are weak signals with inherently low voltage and low frequency, hidden mostly under relatively large high-voltage noise signals. Hence it is extra challenging to analyze them. In modern clinical data analysis, these signals could be further analyzed using conventional machine learning (ML) methods. Also, in the recent past, two-dimensional spectrum-based classification, predominantly with Convolutional Neural Networks (CNN), has been tried with time-series data. One of the objectives of this study is to find which approach would suit better for biomedical signal analysis when data are scarce and signals are weak. Also, in bio-medical signal analysis data is scarce. Yet, to effectively train either an ML or a deep learning (DL) model, a sample clinical dataset of a significant size is required. Hence, the second objective of this research is to present a novel data synthesis method to address data scarcity. With these objectives, the study compares the performance of the time-series-based classification with traditional ML approaches, against the 2D spectrum-based classification for bio-electrical signal classification. For this purpose the study utilizes learning models; Multi-layer Perceptron (MLP), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long Short-Term Memory Networks (LSTMs), Auto Encoder (AE), and Convolutions Neural Network (CNN). Also, the authors propose a novel data synthesis method based on LSTMs to improve the sample size of the standard CHB-MIT Scalp EEG dataset. The results show that with the expanded dataset, the two-dimensional spectrum-based classification architecture was able to achieve a precision level of 85% at the classification. The conventional ML-based methods showed on average a precision level of 82%. In conclusion with the proposed virtual sample generation approach, 2D spectrum-based classification with Convolutional Neural Networks showed promising performances.

Two-Dimensional Silver-Chalcogenolate-Based Cluster-Assembled Material: A p-type Semiconductor.

We have synthesized and characterized a new two-dimensional honeycomb architecture resembling a single-layer of atomically precise silver cluster-assembled material (CAM), [Ag12(StBu)6(CF3COO)6(4,4'-azopyridine)3] (Ag12-azo-bpy). The interlayer noncovalent van der Waals interactions within the single-crystals were successfully disrupted, leading to the creation of this unique structure. The optimized Ag12-azo-bpy CAM demonstrates a valence band that is localized on the Ag12 cluster node situated near the Fermi energy level. This localization induces electron injection from the linker to the cluster node, facilitating efficient charge transportation along the plane. Exploiting this single-layer structure as a distinctive platform for p-type channel material, it was employed in a field-effect transistor configuration. Remarkably, the transistor exhibits a high hole mobility of 1.215 cm2 V-1 s-1 and an impressive ON/OFF current ratio of ∼4500 at room-temperature.

Hybrid Deep Neural Network for Data Driven Missile Guidance with Maneuvering Target

Missile guidance, owing to highly complex and non-linear relative movement between the missile and its target, is a challenging problem. This is further aggravated in case of a maneuvering target which changes its own flight path while attempting to escape the incoming missile. In this study, to achieve computationally superior and accurate missile guidance, a deep learning is employed to propose a self-tuning technique for a fractional-order proportional integral derivative (FOPID) controller of a radar-guided missile chasing an intelligently maneuvering target. A multi-layer two-dimensional architecture is proposed for a deep neural network that combines the prediction feature of recurrent neural networks and estimation feature of feed-forward artificial neural networks. The proposed deep learning based missile guidance scheme is non-intrusive, data-based, and model-free wherein the parameters are optimized on-the-run while predicting the target’s maneuvering tactics to correct for processing time and loop delays of the system. Using deep learning for online optimization with minimal computational burden is the core feature of the proposed technique. Dual-core parallel simulations of missile-target dynamics and the control system were performed to demonstrate superiority of the proposed scheme in feasibility, adaptability, and the ability to effectively minimize the miss-distance in comparison with traditional and neural offline-tuned PID and FOPID based techniques. Compared to state-of-the-art offline-tuned neural control, the miss-distance was reduced by 68.42% for randomly maneuvering targets. Furthermore, a minimum miss-distance of 0.97 m was achieved for intelligently maneuvering targets for which the state-of-the-art method failed to hit the target. Overall, the proposed technique offers a novel approach for addressing the challenges of missile guidance in a computationally efficient and effective manner.

Construction of Well-Defined Two-Dimensional Architectures of Trimetallic Metal-Organic Frameworks for High-Performance Symmetric Supercapacitors.

The high surface-to-volume ratio and extraordinarily large-surface area of two-dimensional (2D) metal-organic framework (MOF) architectures have drawn particular interest for use in supercapacitors. To achieve an excellent electrode material for supercapacitors, well-defined 2D nanostructures of novel trimetallic MOFs were developed for supercapacitor applications. Multivariate MOFs (terephthalate and trimesate MOF) with distinctive nanobrick and nanoplate-like structures were successfully synthesized using a straightforward one-step reflux condensation method by combining Ni, Co, and Zn metal species in equimolar ratios with two different ligands. Furthermore, the effects of the tricarboxylic and dicarboxylic ligands on cyclic voltammetry, charge-discharge cycling, and electrochemical impedance spectroscopy were studied. The derived terephthalate and trimesate MOFs are supported with stainless-steel mesh and provide a suitable electrolyte environment for rapid faradaic reactions with an elevated specific capacity, excellent rate capability, and exceptional cycling stability. It shows a specific capacitance of 582.8 F g-1, a good energy density of 40.47 W h kg-1, and a power density of 687.5 W kg-1 at 5 mA cm-2 with an excellent cyclic stability of 92.44% for 3000 charge-discharge cycles. A symmetric BDC-MOF//BDC-MOF supercapacitor device shows a specific capacitance of 95.22 F g-1 with low capacitance decay, high energy, and power densities which is used for electronic applications. These brand-new trimetallic MOFs display outstanding electrochemical performance and provide a novel strategy for systematically developing high-efficiency energy storage systems.

Synthesis and assembly of two-dimensional heterostructured architectures

Synthesis and assembly of two-dimensional heterostructured architectures

Dimensionality Engineering of Single-Atom Nanozyme for Efficient Peroxidase-Mimicking.

In nature, enzymatic reactions occur in well-functioning catalytic pockets, where substrates bind and react by properly arranging the catalytic sites and amino acids in a three-dimensional (3D) space. Single-atom nanozymes (SAzymes) are a new type of nanozymes with active sites similar to those of natural metalloenzymes. However, the catalytic centers in current SAzymes are two-dimensional (2D) architectures and the lack of collaborative substrate-binding features limits their catalytic activity. Herein, we report a dimensionality engineering strategy to convert conventional 2D Fe-N-4 centers into 3D structures by integrating oxidized sulfur functionalities onto the carbon plane. Our results suggest that oxidized sulfur functionalities could serve as binding sites for assisting substrate orientation and facilitating the desorption of H2O, resulting in an outstanding specific activity of up to 119.77 U mg-1, which is 6.8 times higher than that of conventional FeN4C SAzymes. This study paves the way for the rational design of highly active single-atom nanozymes.

Automated segmentation of the human supraclavicular fat depot via deep neural network in water-fat separated magnetic resonance images.

Human brown adipose tissue (BAT), mostly located in the cervical/supraclavicular region, is a promising target in obesity treatment. Magnetic resonance imaging (MRI) allows for mapping the fat content quantitatively. However, due to the complex heterogeneous distribution of BAT, it has been difficult to establish a standardized segmentation routine based on magnetic resonance (MR) images. Here, we suggest using a multi-modal deep neural network to detect the supraclavicular fat pocket. A total of 50 healthy subjects [median age/body mass index (BMI) =36 years/24.3 kg/m2] underwent MRI scans of the neck region on a 3 T Ingenia scanner (Philips Healthcare, Best, Netherlands). Manual segmentations following fixed rules for anatomical borders were used as ground truth labels. A deep learning-based method (termed as BAT-Net) was proposed for the segmentation of BAT on MRI scans. It jointly leveraged two-dimensional (2D) and three-dimensional (3D) convolutional neural network (CNN) architectures to efficiently encode the multi-modal and 3D context information from multi-modal MRI scans of the supraclavicular region. We compared the performance of BAT-Net to that of 2D U-Net and 3D U-Net. For 2D U-Net, we analyzed the performance difference of implementing 2D U-Net in three different planes, denoted as 2D U-Net (axial), 2D U-Net (coronal), and 2D U-Net (sagittal). The proposed model achieved an average dice similarity coefficient (DSC) of 0.878 with a standard deviation of 0.020. The volume segmented by the network was smaller compared to the ground truth labels by 9.20 mL on average with a mean absolute increase in proton density fat fraction (PDFF) inside the segmented regions of 1.19 percentage points. The BAT-Net outperformed all implemented 2D U-Nets and the 3D U-Nets with average DSC enhancement ranging from 0.016 to 0.023. The current work integrates a deep neural network-based segmentation into the automated segmentation of supraclavicular fat depot for quantitative evaluation of BAT. Experiments show that the presented multi-modal method benefits from leveraging both 2D and 3D CNN architecture and outperforms the independent use of 2D or 3D networks. Deep learning-based segmentation methods show potential towards a fully automated segmentation of the supraclavicular fat depot.

Two Bisligand-Coordinated Luminescent Zn(II)-Coordination Polymers for Sensing of Ions and Pesticides in Aqueous Solutions

Two coordination polymers (CPs) [Zn(PTA)(DTP)(H2O)2]•(DMF) (CP-1) and [Zn(BTC)(DTP)]•(CH3CN)1.5•(H2O)4 (CP-2) with one- and two-dimensional architectures were synthesized from Zn(II) ion and different organic linkers like terephthalic acid (H2PTA), benzene-1,3,5-tricarboxylic acid (H3BTC), and 3,5-di(1,2,4-triazol-1-yl) pyridine (DTP). The fluorescent sensing experiments showed that the two CPs displayed effective, sensitive, and selective abilities towards Fe3+ and Cr2O72-. For sensing the pesticides, CP-1 outperforms in sensing of metamitron (MMT) and CP-2 is ultrasensitive towards imidacloprid (IMI). The possible mechanisms involved in the quenching of the fluorescence intensity include the inner filter effect (IFE) and the fluorescence resonance energy transfer (FRET) effect.

Hollow Spherical NiCo2S4@N-CNT Composites with High Energy Density for All-Solid-State Supercapacitors

NiCo2S4@N-CNT composites with multidimensional hierarchical structures are rationally designed and synthesized by a simple two-step hydrothermal strategy, aiming to achieve excellent electrochemical performances for supercapacitor applications. The hollow spherical NiCo2S4 nanoparticles with both zero- and two-dimensional architecture are interconnected by the conductive bridges of nitrogen-doped carbon nanotubes (N-CNTs, 1D). This unique structure can provide a much rougher surface, rich two-phase interface, and porous channels exposed to electrolytes with fast ion diffusions and electron transmissions, as well as effectively relieve the expansion/contractions during charging and discharging. As such, the asymmetric all-solid-state supercapacitor device assembled by NiCo2S4@N-CNTs and activated carbons can provide an energy density high up to 59.37 W h kg–1 at the power density (750 W kg–1). Even if the power density is increased up to 1.5 kW kg–1, it still can achieve the energy density superior to 45.6 W h kg–1. These results can provide the open interesting perspective to design the favorable structure of bimetallic sulfides for supercapacitor applications.

A FPGA-Based Architecture for Real-Time Cluster Finding in the LHCb Silicon Pixel Detector

This article describes a custom VHDL firmware implementation of a two-dimensional cluster-finder architecture for reconstructing hit positions in the new vertex pixel detector (VELO) that is part of the LHCb Upgrade. This firmware has been deployed to the existing FPGA cards that perform the readout of the VELO, as a further enhancement of the DAQ system, and will run in real time during physics data taking, reconstructing VELO hits coordinates on-the-fly at the LHC collision rate. This pre-processing allows the first level of the software trigger to accept a 11% higher rate of events, as the ready-made hits coordinates accelerate the track reconstruction and consumes significantly less electrical power. It additionally allows the raw pixel data to be dropped at the readout level, thus saving approximately 14% of the DAQ bandwidth. Detailed simulation studies have shown that the use of this real-time cluster finding does not introduce any appreciable degradation in the tracking performance in comparison to a full-fledged software implementation. This work is part of a wider effort aimed at boosting the real-time processing capability of HEP experiments by delegating intensive tasks to dedicated computing accelerators deployed at the earliest stages of the data acquisition chain.

Full two-dimensional ambipolar CFET-like architecture for switchable logic circuits

As the scaling of integrated circuits based on silicon semiconductors becomes increasingly challenging due to the minimum feature size being close to the physical limit, the urgent demand for alternative strategies has fuelled the rapid growth of techniques and material innovations. Here, we report on the fabrication of vertically stacked ambipolar complementary field-effect transistor that is fully composed of two-dimensional materials of WSe2/h-BN/graphene/h-BN/WSe2 heterostructures. The ambipolar feature of the top and bottom WSe2 FET enables a switchable inverter behavior with a favorable voltage gain of up to 75, which can work in both the first and third quadrants. Based on the switchable characteristics, a large voltage swing circuit for single photon avalanche detectors is proposed without any bulky negative-voltage components. This work could open a new pathway for future two-dimensional electronics and ultimate monolithic 3D high-density integration circuits.

Enhanced Light Scattering Using a Two-Dimensional Quasicrystal-Decorated 3D-Printed Nature-Inspired Bio-photonic Architecture

A number of strategies have been exploited so far to trap photons inside living cells to obtain high-contrast imaging. Also, launching light inside biological materials is technically challenging. Using photon confinement in a three-dimensional (3D)-printed biomimetic architecture in the presence of a localized surface plasmon resonance (LSPR) promoter can overcome some of these issues. This work compares optical confinement in natural and 3D-printed photonic architectures, namely, fish scale, in the presence of atomically thin Al70Co10Fe5Ni10Cu5 quasicrystals (QCs). Due to their wideband LSPR response, the QCs work as photon scattering hotspots. The architecture acts as an additive source of excitation for the two-dimensional (2D) QCs via total internal reflection (TIR). The computational analysis describes the surface plasmon-based scattering property of 2D QCs. The 3D-printed fish scale’s image contrast with the 2D Al70Co10Fe5Ni10Cu5 QC has been compared with other 2D materials (graphene, h-BN, and MoS2) and outperforms them. The present study conceptually presents a new approach for obtaining high-quality imaging of biological imaging, even using high-energy photons.

Selective aerobic oxidation of benzyl alcohol under ambient conditions using polyionic nanoclay-supported gold/palladium nanoparticles

Oxidation of alcohols to their respective aldehydes, esters, and acids is a common value-added industrial process, though techniques often rely on harsh reaction conditions (e.g., high temperature, strong base, organic solvents, hydrothermal processes) to achieve rapid reaction turnovers. The incorporation of gold and palladium nanoparticles facilitates the dehydrogenation and O2 reduction processes, respectively, and these advantages can be dually incorporated and enhanced using supported nanoparticle alloys. Herein, we utilize polyionic nanoclays (PINCs) as two-dimensional inorganic-organic architectures for supporting monometallic and bimetallic gold and palladium nanocatalysts for the model oxidation of benzyl alcohol to the corresponding aldehyde, ester, and acid. In addition, we present two new PINC surface chemistries (N-methylpyrrolidinium- and pyridinium-functionalized PINCs) and compare their performances as catalyst supports to show that catalyst enhancement is altered by the PINC surface chemistry. Reaction conditions are mild (aqueous, room temperature, open to atmosphere, weak or no base) yet reaction turnovers are comparable to those of more demanding literature accounts.

Two-Dimensional Covalent Framework Derived Nonprecious Transition Metal Single-Atomic-Site Electrocatalyst toward High-Efficiency Oxygen Reduction

Designing an active, stable, and nonprecious metal catalyst substitute for Pt in the oxygen reduction reaction (ORR) is highly demanded for energy-efficient and cost-effective prototype devices. Single-atomic-site catalysts (SASCs) have been widely concerning because of their maximum atomic utilization and precise structural regulation. Despite being challenging, the controllable synthesis of SASCs is crucial for optimizing ORR activity. Here, we demonstrate an ultrathin organometallic framework template-assisted pyrolysis strategy to synthesize SASCs with a unique two-dimensional (2D) architecture. Electrochemical measurements revealed that Fe-SASCs displayed an excellent ORR activity in an alkaline media, having a half-wave potential and a diffusion-limited current density comparable to those of commercial Pt/C. Remarkably, the durability and methanol tolerance of Fe-SASCs were even superior to those of Pt/C. Furthermore, Fe-SASCs displayed a maximum power density of 142 mW cm-2 with a current density of 235 mA cm-2 as a cathode catalyst in a zinc-air battery, showing its great potential for practical applications.

Halogen bonding: a designer strategy for graphyne-like two-dimensional architectures

Halogen bonding: a designer strategy for graphyne-like two-dimensional architectures

Engineering the MoS2 /MXene Heterostructure for Precise and Noninvasive Diagnosis of Prostate Cancer with Clinical Specimens.

High-throughput metabolic fingerprinting has been deemed one of the most promising strategies for addressing the high false positive rate of prostate cancer (PCa) diagnosis in the prostate-specific antigen (PSA) gray zone. However, the current metabolic fingerprinting remains challenging in achieving high-precision metabolite detection in complex biological samples (e.g., serum and urine). Herein, a novel self-assembly MoS2 /MXene heterostructure nanocomposite with a tailored doping ratio of 10% is presented as a matrix for laser desorption ionization mass spectrometry analysis in clinical biosamples. Notably, owing to the two-dimensional architecture and doping effect, MoS2 /MXene demonstrates favorable laser desorption ionization performance with low adsorption energy, which is evidenced by efficient urinary metabolic fingerprinting with an enhanced area under curve (AUC) diagnosis capability of 0.959 relative to that of serum metabolic fingerprinting (AUC = 0.902) for the diagnosis of PCa in the PSA gray zone. Thus, this MoS2 /MXene heterostructure is anticipated to offer a novel strategy to precisely and noninvasively diagnose PCa in the PSA gray zone.

A Method Based on Timing Weight Priority and Distance Optimization for Quantum Circuit Transformation

In order to implement a quantum circuit on an NISQ device, it must be transformed into a functionally equivalent circuit that satisfies the device's connectivity constraints. However, NISQ devices are inherently noisy, and minimizing the number of SWAP gates added to the circuit is crucial for reducing computation errors. To achieve this, we propose a subgraph isomorphism algorithm based on the timing weight priority of quantum gates, which provides a better initial mapping for a specific two-dimensional quantum architecture. Additionally, we introduce a heuristic swap sequence selection optimization algorithm that uses a distance optimization measurement function to select the ideal sequence and reduce the number of SWAP gates, thereby optimizing the circuit transformation. Our experiments demonstrate that our proposed algorithm is effective for most benchmark quantum circuits, with a maximum optimization rate of up to 43.51% and an average optimization rate of 13.51%, outperforming existing related methods.

Hydrazine High-Performance Oxidation and Sensing Using a Copper Oxide Nanosheet Electrocatalyst Prepared via a Foam-Surfactant Dual Template.

This work demonstrates hydrazine electro-oxidation and sensing using an ultrathin copper oxide nanosheet (CuO-NS) architecture prepared via a versatile foam-surfactant dual template (FSDT) approach. CuO-NS was synthesised by chemical deposition of the hexagonal surfactant Brij®58 liquid crystal template containing dissolved copper ions using hydrogen foam that was concurrently generated by a sodium borohydride reducing agent. The physical characterisations of the CuO-NS showed the formation of a two-dimensional (2D) ultrathin nanosheet architecture of crystalline CuO with a specific surface area of ~39 m2/g. The electrochemical CuO-NS oxidation and sensing performance for hydrazine oxidation revealed that the CuO nanosheets had a superior oxidation performance compared with bare-CuO, and the reported state-of-the-art catalysts had a high hydrazine sensitivity of 1.47 mA/cm2 mM, a low detection limit of 15 μM (S/N = 3), and a linear concentration range of up to 45 mM. Moreover, CuO-NS shows considerable potential for the practical use of hydrazine detection in tap and bottled water samples with a good recovery achieved. Furthermore, the foam-surfactant dual template (FSDT) one-pot synthesis approach could be used to produce a wide range of nanomaterials with various compositions and nanoarchitectures at ambient conditions for boosting the electrochemical catalytic reactions.

Ultrahigh-performance hybrid supercapacitors based on sodium sulfate template-derived electrodes with surface modulation

Exploring efficient and durable electrode materials is crucial for the mature application of high-performance hybrid supercapacitors. Herein, the Fe3O4/Fe nanoparticles with surface phosphate ions modulation (P-Fe3O4/Fe) are successfully deposited on the two-dimensional carbon nanosheets (CNS) through a solid-state sodium sulfate template-directed two-step calcination strategy. Benefiting from the synergistic effect of each component and the unique two-dimensional architecture, the prepared P-Fe3O4/Fe/CNS delivers excellent energy storage features with splendid specific capacitance (427.4 F g−1 at 1 A g−1) and outstanding cycling stability at 15 A g−1 (90.8% retention after 10,000 cycles). Following, phosphate functionalized Co9S8/CoS (P-Co9S8/CoS) nanoparticles anchored on CNS are also fabricated by the same synthesis route, which deliver a maximum specific capacity of 544.6 C g−1 at 1 A g−1 and long cyclic retention of 91.6% after 10,000 cycles at 15 A g−1. By coupling with P-Fe3O4/Fe/CNS and P-Co9S8/CoS/CNS electrodes, the assembled P-Co9S8/CoS/CNS//P-Fe3O4/Fe/CNS hybrid supercapacitor depicts an admirable energy density of 73.4 Wh kg−1 at 1041.7 W kg−1 in alkaline electrolyte and ultralong cycling stability up to 20,000 cycles with only 10.5% capacitance decay. Furthermore, the red light-emitting diode (LED) can be successfully operated for up to 45 min by the series connection of the two devices. This work proposes an efficient strategy to construct ultrahigh-performance hybrid supercapacitors with advanced sodium sulfate template-derived electrodes by phosphate functionalization.