Direct Signal Research Articles

Presubicular VIP expressing interneurons receive facilitating excitation from anterior thalamus

The presubiculum is part of the parahippocampal cortex and plays a fundamental role for orientation in space. Many principal neurons of the presubiculum signal head direction, and show persistent firing when the head of an animal is oriented in a specific preferred direction. GABAergic neurons of the presubiculum control the timing, sensitivity and selectivity of head directional signals from the anterior thalamic nuclei. However, the role of vasoactive intestinal peptide (VIP) expressing interneurons in the presubicular microcircuit has not yet been addressed. Here, we examined the intrinsic properties of VIP interneurons as well as their input connectivity following photostimulation of anterior thalamic axons. We show that presubicular VIP interneurons are more densely distributed in superficial than in deep layers. They are highly excitable. Three groups emerged from the unsupervised cluster analysis of their electrophysiological properties. We demonstrate a frequency dependent recruitment of VIP cells by thalamic afferences and facilitating synaptic input dynamics. Our data provide initial insight into the contribution of VIP interneurons for the integration of thalamic head direction information in the presubiculum.

PhotoSolver: A bidirectional photonic solver for systems of linear equations

Solving systems of linear equations is one of the most fundamental challenges in computing, with ubiquitous applications in science and engineering. Solving large-scale linear equations is particularly challenging, requiring tremendous computing expenses. However, conventional solvers in electronic digital computers face inevitable physical bottlenecks that hinder further advancements in electronic chips. Unlike the electronic architectures, optical computing harnesses the inherent advantages of lights, such as ultimately high speed, negligible energy consumption, and high parallelism, making optical architectures attractive sources for ultra-high speed analog computing. Especially, recent advances in integrated photonic circuits bring exciting new possibilities for high performance computing based on photonic chips. In this work, we propose a bidirectional PhotoSolver using the propagation of optical signals in different directions on a single integrated photonic chip to perform multiple operations in solving systems of linear equations, scalable to solve large-scale systems. Specifically, we propose an in situ approach to iteratively update solutions by performing the forward and backward propagations within a single photonic chip. Furthermore, we propose a partitioning approach to solve large-scale systems using arrays of photonic chips. Numerical experiments demonstrate these capabilities with representative systems of linear equations. By utilizing lights propagating in integrated photonic chips, our PhotoSolvers provide a promising computing architecture to solve large systems of linear equations, with potentials in wide computational applications.

Direct Growth of Vertical Graphene on Fiber Electrodes and Its Application in Alternating Current Line-Filtering Capacitors.

Fiber-shaped electrochemical capacitors (FSECs) have garnered substantial attention to emerging portable, flexible, and wearable electronic devices. However, achieving high electronic and ionic conductivity in fiber electrodes while maintaining a large specific surface area is still a challenge for enhancing the capacitance and rapid response of FSECs. Here, we present an electric-field-assisted cold-wall plasma-enhanced chemical vapor (EFCW-PECVD) method for direct growth of vertical graphene (VG) on fiber electrodes, which is incorporated in the FSECs. The customized reactor mainly consists of two radio frequency coils: one for plasma generation and the other for substrate heating. Precise temperature control can be achieved by adjusting the conductive plates and the applied power. With induction heating, only the substrate is heated to above 500 °C within just 5 min, maintaining a low temperature in the gas phase for the growth of VG with a high quality. Using this method, VG was easily grown on metallic fibers. The VG-coated titanium fibers for FSECs exhibit an ultrahigh rate performance and quick ion transport, enabling the conversion of an alternating current signal to a direct current signal and demonstrating outstanding filtering capabilities.

Outward askew endodermal cell divisions reveal INFLORESCENCE AND ROOT APICES RECEPTOR KINASE functions in division orientation.

Oriented cell divisions establish plant tissue and organ patterning and produce different cell types; this is particularly true of the highly organized Arabidopsis (Arabidopsis thaliana) root meristem. Mutant alleles of INFLORESCENCE AND ROOT APICES RECEPTOR KINASE (IRK) exhibit excess cell divisions in the root endodermis. IRK is a transmembrane receptor kinase that localizes to the outer polar domain of these cells, suggesting that directional signal perception is necessary to repress endodermal cell division. Here, a detailed examination revealed many of the excess endodermal divisions in irk have division planes that specifically skew towards the outer lateral side. Therefore, we termed them 'outward askew' divisions. Expression of an IRK truncation lacking the kinase domain retains polar localization and prevents outward askew divisions in irk; however, the roots exhibit excess periclinal endodermal divisions. Using cell identity markers, we show that the daughters of outward askew divisions transition from endodermal to cortical identity similar to those of periclinal divisions. These results extend the requirement for IRK beyond repression of cell division activity to include cell division plane positioning. Based on its polarity, we propose that IRK at the outer lateral endodermal cell face participates in division plane positioning to ensure normal root ground tissue patterning.

Galvanotactic directionality of cell groups depends on group size.

Motile cells migrate directionally in electric fields. This behavior - galvanotaxis - is important in many physiological phenomena. Individual fish keratocytes migrate to the cathode, while inhibition of PI3K reverses single cells to the anode. Uninhibited cell groups move to the cathode. Surprisingly, groups of PI3K-inhibited cells exhibit bidirectional behavior: larger/smaller groups move to the cathode/anode, respectively. A mechanical model suggests that inner and outer cells interpret directional signals differently, and that a tug-of-war between the outer and inner cells directs the cell groups. These results shed light on general principles of collective cell migration.

Quantum Enhanced Balanced Heterodyne Readout for Differential Interferometry.

Conventional heterodyne readout schemes are now under reconsideration due to the realization of techniques to evade its inherent 3dB signal-to-noise penalty. The application of high-frequency, quadrature-entangled, two-mode squeezed states can further improve the readout sensitivity of audio-band signals. In this Letter, we experimentally demonstrate quantum-enhanced heterodyne readout of two spatially distinct interferometers with direct optical signal combination, circumventing the 3dB heterodyne signal-to-noise penalty. Applying a high-frequency, quadrature-entangled, two-mode squeezed state, we show further signal-to-noise improvement of an injected audio band signal of 3.5dB. This technique is applicable for quantum-limited high-precision experiments, with application to searches for quantum gravity, searches for dark matter, gravitational wave detection, and wavelength-multiplexed quantum communication.

Applied Machine Learning to Study the Movement of Air Masses in the Wind Farm Area

Modeling the atmospheric boundary layer (ABL) in the area of a wind farm using computational fluid dynamics (CFD) methods allows us to study the characteristics of air movement, the shading effect, the influence of relief, etc., and can be actively used in studies of local territories where powerful wind farms are planned to be located. The operating modes of a wind farm largely depend on meteorological phenomena, the intensity and duration of which cause suboptimal operating modes of wind farms, which require the use of modern tools for forecasting and classifying precipitation. The methods and approaches used to predict meteorological phenomena are well known. However, for designed and operated wind farms, the influence of meteorological phenomena on the operating modes, such as freezing rain and hail, remains an urgent problem. This study presents a multi-layered neural network for the classification of precipitation zones, designed to identify adverse meteorological phenomena for wind farms according to weather stations. The neural network receives ten inputs and has direct signal propagation between six hidden layers. During the training of the neural network, an overall accuracy of 81.78%, macro-average memorization of 81.07%, and macro-average memorization of 75.05% were achieved. The neural network is part of an analytical module for making decisions on the application of control actions (control of the boundary layer of the atmosphere by injection of silver iodide, ionization, etc.) and the formation of the initial conditions for CFD modeling. Using the example of the Ulyanovsk wind farm, a study on the movement of air masses in the area of the wind farm was conducted using the initial conditions of the neural network. Digital models of wind turbines and terrain were created in the Simcenter STAR-CCM+ software package, version 2022.1; an approach based on a LES model using an actuating drive disk model (ADM) was implemented for modeling, allowing calculation with an error not exceeding 5%. According to the results of the modeling of the current layout of the wind turbines of the Ulyanovsk wind farm, a significant overlap of the turbulent wake of the wind turbines and an increase in the speed deficit in the area of the wind farm were noted, which significantly reduced its efficiency. A shortage of speed in the near and far tracks was determined for special cases of group placement of wind turbines.

NMSSM explanation for excesses in the search for neutralinos and charginos and a 95 GeV Higgs boson

The observed excesses in the search for neutralinos and charginos by ATLAS and CMS can be fitted simultaneously in the minimal supersymmetric standard model (MSSM) assuming a light higgsino mass, of magnitude less than about 250 GeV, and a compressed higgsino dominated neutralino and chargino spectrum, with 5–10% mass splittings. However, light higgsinos as dark matter would have far too large direct detection cross sections. We consider the Next-to-MSSM (NMSSM) with an additional singlino-like lightest supersymmetric particle (LSP) a few GeV below the next-to-lightest supersymmetric particle (NLSP). Sparticles prefer to decay first into the NLSP and remnants from the final decay into the LSP are too soft to contribute to the observed signals. Co-annihilation in the higgsino-sector can generate a relic density in the WMAP/Planck window. The singlino-like LSP has automatically a direct detection cross section below present and future sensitivities: a direct detection signal in the near future would exclude this scenario. The singlet-like Higgs scalar of the NMSSM can have a mass around 95 GeV and signal cross sections in the bb¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$b\\bar{b}$$\\end{document} channel at LEP and in the γγ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma \\gamma $$\\end{document} channel at the LHC compatible with the respective observations.

STAR-RIS aided NOMA Networks: A Signal Enhancement Algorithm

In order to overcome the reflecting-only issue of the conventional reconfigurable intelligent surface (RIS), simultaneous-transmitting-and-reflecting reconfigurable intelligent surface (STAR-RIS) stands as a potential solution for providing access services to the users located at the full space. By utilizing the omnidirectional properties of STAR-RIS, non-orthogonal multiple access (NOMA) users on both sides can be served simultaneously. By making the signal reflected by STAR-RIS coherent superposition with the direct signal, using the Riemannian optimization algorithm (ROA), the proposed signal enhancement algorithm can greatly enhance the signal power of NOMA users, reduce the outage probability, and improve the ergodic rate performance. According to the simulation results, the random phase STAR-RIS can improve the spectral efficiency of 3.6bit/s/Hz compared without STAR-RIS. After using the proposed ROA, the spectral efficiency can be extra increased by 1.8bit/s/Hz, which verifies the superiority of the proposed algorithm.

Fatigue Crack Monitoring Method Based on the Lamb Wave Damage Index.

For practical engineering structures, fatigue is one of the main factors affecting their safety and durability. Under long-term service conditions, the minor damage will be affected by fatigue loading and expand to macroscopic cracks, affecting the structure's service performance. Based on the sensitivity of Lamb waves to minor and initial damage, a damage monitoring method for fatigue crack propagation is proposed. By carrying out fatigue crack propagation tests under constant amplitude loading, the Paris equation of 316L steel and damage signals at different crack growth stages were obtained. Combined with damage monitoring tests and finite element analysis, the relationship between the phase damage index (PDI), amplitude damage index (ADI), signal correlation coefficient, and fatigue crack propagation length was studied. Compared with PDI and ADI, the signal correlation coefficient is more sensitive to crack initiation, which can be selected as the damage monitoring index in the initial stage of crack growth. With the increase of fatigue crack propagation length, the peak time of the direct wave signal gradually moves backward, which shows an obvious phase change. In the whole fatigue crack growth stage, PDI and crack length show a monotonically changing trend. By using the stress intensity factor as the conversion parameter, a prediction model of the fatigue crack propagation rate based on PDI was established. Compared to the fatigue crack propagation rate measured by experiments, the relative error of the predicted results is 10%, which verifies the accuracy of the proposed damage monitoring method.

Anterior cingulate cortex lesions impair multiple facets of task engagement not mediated by dorsomedial striatum neuron firing.

The anterior cingulate cortex (ACC) has been implicated across multiple highly specialized cognitive functions-including task engagement, motivation, error detection, attention allocation, value processing, and action selection. Here, we ask if ACC lesions disrupt task performance and firing in dorsomedial striatum (DMS) during the performance of a reward-guided decision-making task that engages many of these cognitive functions. We found that ACC lesions impacted several facets of task performance-including decreasing the initiation and completion of trials, slowing reaction times, and resulting in suboptimal and inaccurate action selection. Reductions in movement times towards the end of behavioral sessions further suggested attenuations in motivation, which paralleled reductions in directional action selection signals in the DMS that were observed later in recording sessions. Surprisingly, however, beyond altered action signals late in sessions-neural correlates in the DMS were largely unaffected, even though behavior was disrupted at multiple levels. We conclude that ACC lesions result in overall deficits in task engagement that impact multiple facets of task performance during our reward-guided decision-making task, which-beyond impacting motivated action signals-arise from dysregulated attentional signals in the ACC and are mediated via downstream targets other than DMS.

A new method for tracking the preparatory activation of target templates for visual search with high temporal precision.

Efficiently selecting task-relevant objects during visual search depends on foreknowledge of their defining characteristics, which are represented within attentional templates. These templates bias attentional processing toward template-matching sensory signals and are assumed to become anticipatorily activated prior to search display onset. However, a direct neural signal for such preparatory template activation processes has so far remained elusive. Here, we introduce a new high-definition rapid serial probe presentation paradigm (RSPP-HD), which facilitates high temporal resolution tracking of target template activation processes in real time via monitoring of the N2pc component. In the RSPP-HD procedure, task-irrelevant probe displays are presented in rapid succession throughout the period between task-relevant search displays. The probe and search displays are homologously formed by lateralized "clouds" of colored dots, yielding probes that occur at task-relevant locations without confounding template-guided and salience-driven attentional shifts. Target color probes appearing at times when a corresponding target template is active should attract attention, thereby eliciting an N2pc. In a condition where new probe displays appeared every 50 ms, probe N2pcs were reliably elicited during the final 800 ms prior to search display onset, increasing in amplitude toward the end of this preparation period. Analogous temporal profiles were also observed with longer intervals between probes. These findings show that search template activation processes are transient and that their temporal profile can be reliably monitored at high-sampling frequencies with the RSPP-HD paradigm. This procedure offers a new route to approach various questions regarding the content and temporal dynamics of attentional control processes.

Retinoic acid signalling regulates branchiomeric neck muscle development at the head/trunk interface.

Skeletal muscles of the head and trunk originate in distinct lineages with divergent regulatory programmes converging on activation of myogenic determination factors. Branchiomeric head and neck muscles share a common origin with cardiac progenitor cells in cardiopharyngeal mesoderm (CPM). The retinoic acid (RA) signalling pathway is required during a defined early time window for normal deployment of cells from posterior CPM to the heart. Here, we show that blocking RA signalling in the early mouse embryo also results in selective loss of the trapezius neck muscle, without affecting other skeletal muscles. RA signalling is required for robust expression of myogenic determination factors in posterior CPM and subsequent expansion of the trapezius primordium. Lineage-specific activation of a dominant-negative RA receptor reveals that trapezius development is not regulated by direct RA signalling to myogenic progenitor cells in CPM, or through neural crest cells, but indirectly through the somitic lineage, closely apposed with posterior CPM in the early embryo. These findings suggest that trapezius development is dependent on precise spatiotemporal interactions between cranial and somitic mesoderm at the head/trunk interface.

Macroscopic brain dynamics beyond contralateral primary motor cortex for movement prediction

This study investigates the complex relationship between upper limb movement direction and macroscopic neural signals in the brain, which is critical for understanding brain-computer interfaces (BCI). Conventional BCI research has primarily focused on a local area, such as the contralateral primary motor cortex (M1), relying on the population-based decoding method with microelectrode arrays. In contrast, macroscopic approaches such as electroencephalography (EEG) and magnetoencephalography (MEG) utilize numerous electrodes to cover broader brain regions. This study probes the potential differences in the mechanisms of microscopic and macroscopic methods. It is important to determine which neural activities effectively predict movements. To investigate this, we analyzed MEG data from nine right-handed participants while performing arm-reaching tasks. We employed dynamic statistical parametric mapping (dSPM) to estimate source activity and built a decoding model composed of long short-term memory (LSTM) and a multilayer perceptron to predict movement trajectories. This model achieved a high correlation coefficient of 0.79 between actual and predicted trajectories. Subsequently, we identified brain regions sensitive to predicting movement direction using the integrated gradients (IG) method, which assesses the predictive contribution of each source activity. The resulting salience map demonstrated a distribution without significant differences across motor-related regions, including M1. Predictions based solely on M1 activity yielded a correlation coefficient of 0.42, nearly half as effective as predictions incorporating all source activities. This suggests that upper limb movements are influenced by various factors such as movement coordination, planning, body and target position recognition, and control, beyond simple muscle activity. All of the activities are needed in the decoding model using macroscopic signals. Our findings also revealed that contralateral and ipsilateral hemispheres contribute equally to movement prediction, implying that BCIs could potentially benefit patients with brain damage in the contralateral hemisphere by utilizing brain signals from the ipsilateral hemisphere. In conclusion, this study demonstrates that macroscopic activity from large brain regions significantly contributes to predicting upper limb movement. Non-invasive BCI systems would require a comprehensive collection of neural signals from multiple brain regions.

Accidental Encounter of Repair Intermediates in Alkylated DNA May Lead to Double-Strand Breaks in Resting Cells.

In clinics, chemotherapy is often combined with surgery and radiation to increase the chances of curing cancers. In the case of glioblastoma (GBM), patients are treated with a combination of radiotherapy and TMZ over several weeks. Despite its common use, the mechanism of action of the alkylating agent TMZ has not been well understood when it comes to its cytotoxic effects in tumor cells that are mostly non-dividing. The cellular response to alkylating DNA damage is operated by an intricate protein network involving multiple DNA repair pathways and numerous checkpoint proteins that are dependent on the type of DNA lesion, the cell type, and the cellular proliferation state. Among the various alkylating damages, researchers have placed a special on O6-methylguanine (O6-mG). Indeed, this lesion is efficiently removed via direct reversal by O6-methylguanine-DNA methyltransferase (MGMT). As the level of MGMT expression was found to be directly correlated with TMZ efficiency, O6-mG was identified as the critical lesion for TMZ mode of action. Initially, the mode of action of TMZ was proposed as follows: when left on the genome, O6-mG lesions form O6-mG: T mispairs during replication as T is preferentially mis-inserted across O6-mG. These O6-mG: T mispairs are recognized and tentatively repaired by a post-replicative mismatched DNA correction system (i.e., the MMR system). There are two models (futile cycle and direct signaling models) to account for the cytotoxic effects of the O6-mG lesions, both depending upon the functional MMR system in replicating cells. Alternatively, to explain the cytotoxic effects of alkylating agents in non-replicating cells, we have proposed a "repair accident model" whose molecular mechanism is dependent upon crosstalk between the MMR and the base excision repair (BER) systems. The accidental encounter between these two repair systems will cause the formation of cytotoxic DNA double-strand breaks (DSBs). In this review, we summarize these non-exclusive models to explain the cytotoxic effects of alkylating agents and discuss potential strategies to improve the clinical use of alkylating agents.

Cerebral perfusion metrics calculated directly from a hypoxia-induced step change in deoxyhemoglobin

Resting cerebral perfusion metrics can be calculated from the MRI ΔR2* signal during the first passage of an intravascular bolus of a Gadolinium-based contrast agent (GBCA), or more recently, a transient hypoxia-induced change in the concentration of deoxyhemoglobin ([dOHb]). Conventional analysis follows a proxy process that includes deconvolution of an arterial input function (AIF) in a tracer kinetic model. We hypothesized that the step reduction in magnetic susceptibility accompanying a step decrease in [dOHb] that occurs when a single breath of oxygen terminates a brief episode of lung hypoxia permits direct calculation of relative perfusion metrics. The time course of the ΔR2* signal response enables both the discrimination of blood arrival times and the time course of voxel filling. We calculated the perfusion metrics implied by this step signal change in seven healthy volunteers and compared them to those from conventional analyses of GBCA and dOHb using their AIF and indicator dilution theory. Voxel-wise maps of relative cerebral blood flow and relative cerebral blood volume had a high spatial and magnitude congruence for all three analyses (r > 0.9) and were similar in appearance to published maps. The mean (SD) transit times (s) in grey and white matter respectively for the step response (7.4 (1.1), 8.05 (1.71)) were greater than those for GBCA (2.6 (0.45), 3.54 (0.83)) attributable to the nature of their respective calculation models. In conclusion we believe these calculations of perfusion metrics derived directly from ΔR2* have superior merit to calculations via AIF by virtue of being calculated from a direct signal rather than through a proxy model which encompasses errors inherent in designating an AIF and performing deconvolution calculations.

Highly Efficient Hybrid Reconfigurable Intelligent Surface Approach for Power Loss Reduction and Coverage Area Enhancement in 6G Networks

This paper introduces a novel efficient hybrid reconfigurable intelligent surface (RIS) approach designed to significantly reduce power loss and enhance coverage area in 6G networks. The core innovation of this approach lies in an advanced iterative algorithm introduced as the Hybrid reconfigurable intelligent surface decision-making algorithm (HRIS-DMA) that integrates precise user location data into the RIS configuration process. By dynamically adjusting RIS elements to reflect and direct signals based on real-time user positions, this method minimises signal attenuation and optimises signal propagation. The mechanism driving the performance gains includes precise beamforming and intelligent reflection, continuously refined through iterative updates. This technique ensures robust signal strength and expanded coverage, addressing the challenges of dense and diverse deployment scenarios in 6G networks. The proposed scheme’s application in 6G networks demonstrates substantial improvements in signal quality and network reliability, paving the way for enhanced user experiences and efficient communication infrastructures. This novel approach was tested using MATLAB R2023a , and its performance was evaluated using three downlink scenarios: zero to few, few to moderate, and moderate to many obstacles. The three scenarios show higher coverages than conventional simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) and base station (BS) handover. Based on the evaluation metrics, the analysis results of the novel HRIS-DMA show 70% less signal power loss, 0.17 μs less system delay, 25 dB and 12 dB channel gain compared with the conventional STAR-RIS and BS handover, respectively, and 95% improvement in the overall system’s efficiency compared to STAR-RIS and 13% compared to BS-BS handover.

STUDY AND ANALYSIS OF FETAL HEART RATE MONITORING SYSTEM

Analysis of FHR variability, together with uterine muscle contractions and fetal movement activity, subject to cardiotocographic monitoring, currently play a fundamental role in fetal assessment. The strength of cardiotocography lies in that normal ranges of the results of the FHR signal analysis almost always (>95%) confirm fetal wellbeing. Unfortunately, questionable or abnormal signal features may indicate both fetal distress and its absence. The classic fetal heart rate variability analysis consists in determining the FHR baseline and then in identifying bradycardia/tachycardia, acceleration/deceleration patterns as well as the type and amplitude of oscillations. A more advanced FHR analysis is aimed at evaluating instantaneous FHR variability, especially with beat-to-beat approach, which is very important in fetal condition assessment. The most common technique of recording the FHR signal is the Doppler ultrasound method, which makes it difficult to correctly estimate the instantaneous heart rate variability because the measurements are averaged. On the other hand, instantaneous heart rate variability plays a crucial role in the diagnosis of fetal condition [1]. The increased interest in monitoring the fetal cardiac bioelectrical activity is due to the fact that the analysis of the fetal electrocardiogram ensures high efficiency and accuracy of the heart rate measurement even on a beat-to-beat level. The first fetal electrocardiogram was recorded as early as in the 1960-ies by means of a special spiral electrode enabling direct signal registration from a fetal head. Due to the way the electrode is placed, its use is limited only to the advanced labour stage [1]..

Direct electromagnetic information processing with planar diffractive neural network.

Diffractive neural network in electromagnetic wave-driven system has attracted great attention due to its ultrahigh parallel computing capability and energy efficiency. However, recent neural networks based on the diffractive framework still face the bottlenecks of misalignment and relatively large size limiting their further applications. Here, we propose a planar diffractive neural network (pla-NN) with a highly integrated and conformal architecture to achieve direct signal processing in the microwave frequency. On the basis of printed circuit fabrication process, the misalignment could be effectively circumvented while enabling flexible extension for multiple conformal and stacking designs. We first conduct validation on the fashion-MNIST dataset and experimentally build up a system using the proposed network architecture for direct recognition of different geometry structures in the electromagnetic space. We envision that the presented architecture, once combined with the advanced dynamic maneuvering techniques and flexible topology, would exhibit unlimited potentials in the areas of high-performance computing, wireless sensing, and flexible wearable electronics.

Radiation-tolerant dose sensor based on undoped amorphous silicon

Single-crystal silicon diodes are replacing gas-filled dose-rate meters because they are batch-processable, mechanically robust, compact, and operable at low voltages. However, they are prone to radiation damage in high dose fields. Hydrogenated amorphous silicon (a-Si:H) provides the manufacturing and operational benefits of silicon while adding inherent radiation hardness because of its wider bandgap and disordered microstructure. Previously, sensors with tens of micrometers thick a-Si have been investigated for direct dose sensing in x-ray imaging applications. However, the limited charge carrier lifetimes of the medium can reduce the charge collection efficiency and thereby the sensitivity. If a higher density, higher Z active media is coupled to thin amorphous silicon active layers, then an additional indirect component can be added to this direct signal to exhibit ∼15 μGy/s dose-rate sensitivities at zero and low-bias (<0.5 V) operation. Here we show that thin (150, 300, 450 nm) a-Si:H optical sensors can produce external quantum efficiencies above 40% and internal quantum efficiencies that exceed 70 % over a broad band from 380 nm to 650 nm, resulting in dose-rate sensitivity even at 0 V. When the charge-collecting electrodes abutting the a-Si:H serve the dual purpose as reflective layers in a lossy Fabry-Perot cavity, then internal quantum efficiencies greater than 83 % can be realized in a 30 nm thick layer at 410 nm. Cobalt-60 gamma-ray irradiation to 100 kGy (air) shows no radiation-induced degradation for even the thicker 300 nm sample; rather, a radiation hardening effect is observed as evinced in reduced dark current.