Local Field Research Articles

Advanced pyro-/piezoelectric catalytic disinfection of water via porous spontaneously polarized ceramic with strong local electric field induced by micro-nano bubbles

In-situ electroporation disinfection technology inactivates microorganisms by local electric field penetrating antioxidant membranes without producing by-products, which reduces the consumption of oxidants. However, this individual technology exhibits weak disinfection performance for high water fluxes. Herein, a disinfection system based on the synergy of porous spontaneously polarized ceramic (PSPC) and micro-nano bubbles (MNB) (PSPC/MNB disinfection system, PMDS) was constructed for efficient disinfection of drinking water. Pyro-/piezoelectric effects of PSPC induced by MNB collapse constructed a 1.92 × 108 V/m of local electric field (simulated by COMSOL Multiphysics) and generated reactive oxygen species (ROS; •OH, 1O2, •O2−, and H2O2), resulting in electroporation damage on microorganisms and subsequent fatal oxidation of the intracellular contents by ROS. PMDS completely inactivated 107 CFU/mL of E. coli within 45 min, about 10 times faster than the MNB process, which is attributed to that the abundant porous and rough surface in PSPC increased the specific surface area and dipoles for pyro-/piezoelectric catalysis. Moreover, MNB collapse enriched •OH and induced pyro-/piezoelectric effects of PSPC through local temperature and pressure gradients. Synergistic interaction of PSPC/MNB collected additional thermal and mechanical energies besides the collapse of MNB, and the pyro-/piezoelectricity-induced in-situ electroporation–ROS deep oxidation avoided oxidant consumption and hazardous by-products. PMDS presented favorable disinfection efficiency in actual drinking water source, expected to be a practical system for eliminating microbial hazards in drinking water.

A Comparison of Local and Global Strategies for Exploiting Field Inversion on Separated Flows at Low Reynolds Number

The prediction of separated flows at low Reynolds numbers is crucial for several applications in aerospace and energy fields. Reynolds-averaged Navier–Stokes (RANS) equations are widely used but their accuracy is limited in the presence of transition or separation. In this work, two different strategies for improving RANS simulations by means of field inversion are discussed. Both strategies require solving an optimization problem to identify a correction field by minimizing the error on some measurable data. The obtained correction field is exploited with two alternative strategies. The first strategy aims to the identification of a relation that allows to express the local correction field as a function of some local flow features. However, this regression can be difficult or even impossible because the relation between the assumed input variables and the local correction could not be a function. For this reason, an alternative is proposed: a U-Net model is trained on the original and corrected RANS results. In this way, it is possible to perform a prediction with the original RANS model and then correct it by means of the U-Net. The methodologies are evaluated and compared on the flow around the NACA0021 and the SD7003 airfoils.

Equivalence of regular spinor fields

In the Lounesto classification, there are three types of regular spinors. They are classified by the condition that at least one of the scalar or pseudo scalar norms are non-vanishing. The Dirac spinors are regular spinors because their scalar and pseudo scalar norms are non-zero and zero respectively. We construct local and Lorentz-covariant fermionic fields from all three classes of regular spinors. By computing the invariants and bilinear covariants of the regular spinor fields, we show that they are physically equivalent to the Dirac fields in the sense that whatever interactions one writes down using the regular spinor fields, they can always be expressed in terms of the Dirac fields.

MDFA-Net: Multi-Scale Differential Feature Self-Attention Network for Building Change Detection in Remote Sensing Images

Building change detection (BCD) from remote sensing images is an essential field for urban studies. In this well-developed field, Convolutional Neural Networks (CNNs) and Transformer have been leveraged to empower BCD models in handling multi-scale information. However, it is still challenging to accurately detect subtle changes using current models, which has been the main bottleneck to improving detection accuracy. In this paper, a multi-scale differential feature self-attention network (MDFA-Net) is proposed to effectively integrate CNN and Transformer by balancing the global receptive field from the self-attention mechanism and the local receptive field from convolutions. In MDFA-Net, two innovative modules were designed. Particularly, a hierarchical multi-scale dilated convolution (HMDConv) module was proposed to extract local features with hybrid dilation convolutions, which can ameliorate the effect of CNN’s local bias. In addition, a differential feature self-attention (DFA) module was developed to implement the self-attention mechanism at multi-scale difference feature maps to overcome the problem that local details may be lost in the global receptive field in Transformer. The proposed MDFA-Net achieves state-of-the-art accuracy performance in comparison with related works, e.g., USSFC-Net, in three open datasets: WHU-CD, CDD-CD, and LEVIR-CD. Based on the experimental results, MDFA-Net significantly exceeds other models in F1 score, IoU, and overall accuracy; the F1 score is 93.81%, 95.52%, and 91.21% in WHU-CD, CDD-CD, and LEVIR-CD datasets, respectively. Furthermore, MDFA-Net achieved first or second place in precision and recall in the test in all three datasets, which indicates its better balance in precision and recall than other models. We also found that subtle changes, i.e., small-sized building changes and irregular boundary changes, are better detected thanks to the introduction of HMDConv and DFA. To this end, with its better ability to leverage multi-scale differential information than traditional methods, MDFA-Net provides a novel and effective avenue to integrate CNN and Transformer in BCD. Further studies could focus on improving the model’s insensitivity to hyper-parameters and the model’s generalizability in practical applications.

Experience Dependence of Alpha Rhythms and Neural Dynamics in the Mouse Visual Cortex.

The role of experience in the development and maintenance of emergent network properties such as cortical oscillations and states is poorly understood. To define how early-life experience affects cortical dynamics in the visual cortex of adult, head-fixed mice, we examined the effects of two forms of blindness initiated before eye opening and continuing through recording: (1) bilateral loss of retinal input (enucleation) and (2) degradation of visual input (eyelid suture). Neither form of deprivation fundamentally altered the state-dependent regulation of firing rates or local field potentials. However, each deprivation caused unique changes in network behavior. Laminar analysis revealed two different generative mechanisms for low-frequency synchronization: one prevalent during movement and the other during quiet wakefulness. The former was absent in enucleated mice, suggesting a mouse homolog of human alpha oscillations. In addition, neurons in enucleated animals were less correlated and fired more regularly, but no change in mean firing rate. Eyelid suture decreased firing rates during quiet wakefulness, but not during movement, with no effect on neural correlations or regularity. Sutured animals showed a broadband increase in depth EEG power and an increased occurrence, but reduced central frequency, of narrowband gamma oscillations. The complementary-rather than additive-effects of lid suture and enucleation suggest that the development of emergent network properties does not require vision but is plastic to modified input. Our results suggest a complex interaction of internal set points and experience determines mature cortical activity, with low-frequency synchronization being particularly susceptible to early deprivation.

Contribution of basal ganglia activity to REM sleep disorder in Parkinson’s disease

BackgroundRapid eye movement (REM) sleep behaviour disorder (RBD) is one of the most common sleep problems and represents a key prodromal marker in Parkinson’s disease (PD). It remains unclear whether...

Exploring the Spatial Distribution of Local Field Gradients by Tip-Enhanced Raman Mapping of a Single-Molecule Probe

Exploring the Spatial Distribution of Local Field Gradients by Tip-Enhanced Raman Mapping of a Single-Molecule Probe

Electrophysiological and Behavioral Markers of Hyperdopaminergia in DAT-KO Rats.

Background/Objectives: Dopamine dysfunction (DA) is a hallmark of many neurological disorders. In this case, the mechanism of changes in dopamine transmission on behavior remains unclear. This study is a look into the intricate link between disrupted DA signaling, neuronal activity patterns, and behavioral abnormalities in a hyperdopaminergic animal model. Methods: To study the relationship between altered DA levels, neuronal activity, and behavioral deficits, local field potentials (LFPs) were recorded during four different behaviors in dopamine transporter knockout rats (DAT-KO). At the same time, local field potentials were recorded in the striatum and prefrontal cortex. Correlates of LFP and accompanying behavioral patterns in genetically modified (DAT-KO) and control animals were studied. Results: DAT-KO rats exhibited desynchronization between LFPs of the striatum and prefrontal cortex, particularly during exploratory behavior. A suppressive effect of high dopamine levels on the striatum was also observed. Wild-type rats showed greater variability in LFP patterns across certain behaviors, while DAT-KO rats showed more uniform patterns. Conclusions: The decisive role of the synchrony of STR and PFC neurons in the organization of motor acts has been revealed. The greater variability of control animals in certain forms of behavior probably suggests greater adaptability. More uniform patterns in DAT-KO rats, indicating a loss of striatal flexibility when adapting to specific motor tasks. It is likely that hyperdopaminergy in the DAT-KO rat reduces the efficiency of information processing due to less synchronized activity during active behavior.

Sensing data and methodology from the Adaptive DBS Algorithm for Personalized Therapy in Parkinson’s Disease (ADAPT-PD) clinical trial

Adaptive deep brain stimulation (aDBS) is an emerging advancement in DBS technology; however, local field potential (LFP) signal rate detection sufficient for aDBS algorithms and the methods to set-up aDBS have yet to be defined. Here we summarize sensing data and aDBS programming steps associated with the ongoing Adaptive DBS Algorithm for Personalized Therapy in Parkinson’s Disease (ADAPT-PD) pivotal trial (NCT04547712). Sixty-eight patients were enrolled with either subthalamic nucleus or globus pallidus internus DBS leads connected to a Medtronic PerceptTM PC neurostimulator. During the enrollment and screening procedures, a LFP (8–30 Hz, ≥1.2 µVp) control signal was identified by clinicians in 84.8% of patients on medication (65% bilateral signal), and in 92% of patients off medication (78% bilateral signal). The ADAPT-PD trial sensing data indicate a high LFP signal presence in both on and off medication states of these patients, with bilateral signal in the majority, regardless of PD phenotype.

Variations in Clustering of Multielectrode Local Field Potentials in the Motor Cortex of Macaque Monkeys during a Reach-and-Grasp Task.

There is experimental evidence of varying correlation among the elements of the neuromuscular system over the course of the reach-and-grasp task. The aim of this study was to investigate if modifications in correlations and clustering can be detected in the local field potential (LFP) recordings of the motor cortex during the task. To this end, we analyzed the LFP recordings from a previously published study on monkeys that performed a reach-and-grasp task for targets with a vertical or horizontal orientation. LFP signals were recorded from the motor and premotor cortex of macaque monkeys as they performed the task. We found very robust changes in the correlations of the multielectrode LFP recordings that corresponded to task epochs. Mean LFP correlation increased significantly during reach and then decreased during grasp. This pattern was very robust for both left and right arm reaches irrespective of target orientation. A hierarchical cluster analysis also demonstrated similar changes. In focusing on correlations, our study has contributed new insights to the understanding of LFP signals and their relationship to movement. A sliding window computation of the number of clusters was performed to probe the capacities of the LFP clusters for detecting upcoming task events. For a very high percentage of trials (97.89%), there was a downturn in cluster number following the Pellet Drop (GO signal) that reached a minimum preceding the Start of grasp, hence indicating that cluster analyses of LFPs could contribute to signaling an increased probability of the Start of grasp.

Attitude determination for multirotor aerial vehicles using a prescribed‐time super‐twisting algorithm

AbstractThis paper is concerned with the prescribed‐time robust attitude determination (AD) of multirotor aerial vehicles (MAVs) using vector measurements from the local magnetic field and local gravity. To address this problem, we first introduce a novel modified super‐twisting algorithm endowed with the prescribed‐time convergence property. The state of the proposed algorithm is governed by an unbounded time‐varying gain up to the prescribed settling time (PST) and by a function after that. Therefore, after the PST, the new algorithm coincides with the conventional super‐twisting, thus showing robust stability at the origin. This prescribed‐time super‐twisting algorithm (PTSTA) is then applied to the formulation of a three‐stage gyro‐free attitude determination method for MAVs. In the first stage, the classical QUEST algorithm is used to compute a Wahba‐optimal attitude estimate from the vector measurements. In the second stage, the PTSTA is employed in the formulation of a robust state estimator that provides estimates of the attitude Gibbs vector and its rate. Finally, in the third stage, these state estimates as well as the attitude kinematic equation are immediately used to compute the MAV angular velocity. The proposed robust prescribed‐time gyro‐free AD method is evaluated numerically, showing invariance with respect to disturbance and model uncertainty.

An Ultrasensitive Programmable 2D Photoelectric Synaptic Transistor

AbstractThe burgeoning advancement of information technology has engendered a discernible surge in the examination of neuromorphic devices, notably drawing broader attention to artificial vision systems endowed with sensory recognition capabilities. Current photoelectric synapse devices employed in artificial vision systems are generally well‐suited for well‐illuminated conditions, yet exhibit diminished sensitivity in weak‐light scenarios, resulting in a pronounced deterioration of recognition accuracy. Here, an ultrasensitive photoelectric synaptic transistor based on negative quantum capacitance effect resulted from the 2D semi‐metallic graphene layer that partially enclosed within the gate dielectric layer, which manifests a noteworthy reduction in device control voltage and exhibits perception and storage capabilities for weak light of 39.4 nW cm−2 with detectivity above 1016 cm Hz1/2 W−1 is demonstrated. The voltage amplification effect and the concomitant formation of an equivalent local electrostatic field induced by the negative quantum capacitance effect engenders a robust programmable synaptic plasticity for extremely weak light by modifying the control gate. These results represent the inaugural integration of the negative quantum capacitance effect into optoelectronic devices and furnish a robust hardware foundation for developing vision systems in weak‐light environments.

Development and Optimization of Micro‐Nanotopographical Platforms for Surface Enhanced Raman Scattering Biomolecular Detection

AbstractTopologically designed micro‐ and nanostructured surface‐enhanced Raman scattering (SERS) substrates propel the advancements of innovative applications, including environmental and forensic point‐of‐care miniaturised devices via enhancing the localised electric fields for accurate analyte sensing. Herein, a method for designing, optimising and fabricating fine‐tuneable concentric hexagonal, triangular and rectangular SERS‐active micronano‐substrates is developed, with each unit yielding significant enhancement. Numerical simulations of the 3D near‐field electric field guided the optimal design process. While the coaxial SERS substrates consistently outperformed their solid counterparts, the hexagonal micro‐nano topologies exhibited ×21 higher signal than coaxial square arrays and a 12‐15‐fold increase over the triangle structures. Alternation of the topological designs from square to triangle lattice yielded more uniform plasmonic modes propagating along the 60°‐directions with various resonance modes playing key roles in light reflectance. This enables the engineering of platforms with tailor‐enhanced signals by changing the arrangement of micro‐nano patterned coaxial arrays. The fabricated SERS substrates are validated by detecting traumatic brain injury biomarkers, effectively yielding the characteristic fingerprint spectra of each neuro‐molecule. The straightforward development of sub‐micrometre tuneable SERS‐active architectures enables anelegant route for high‐throughput biochemical sensing, laying a platform for amplified bimolecular detection of disease biomarkers and integration in bioanalytical systems.

Tailoring charge transport in BaTiO3 crystals through dislocation engineering

AbstractDislocations in oxide ceramics significantly influence their physical properties by creating substantial local strain fields, new electronic states, and space‐charge layers. In this study, we investigated the effects of mechanically introduced dislocations on the electrical conductivity of BaTiO3 single crystals. High‐temperature plastic deformation was employed to introduce a high dislocation density with a {100}〈100〉 slip system. Impedance measurements revealed a significant anisotropy in the conductivity due to the presence of oriented dislocation structures. The crystals with dislocation lines aligned parallel to the measurement axis ([001] crystallographic direction) exhibited 16‐fold higher conductivity compared to those measured across the dislocations. Compared to the pristine crystals, this means an increase in conductivity when the measurements were carried out parallel to dislocation lines and a decrease in perpendicular measurements. Our study demonstrates that not only ferroelectric properties but also charge transport can be modified by dislocation introduction in BaTiO3.

Mourre Theory and Asymptotic Observables in Local Relativistic Quantum Field Theory

We prove the convergence of Araki–Haag detectors in any Haag–Kastler quantum field theory with an upper and lower mass gap. We cover the case of a single Araki–Haag detector on states of bounded energy, which are selected from the absolutely continuous part of the energy-momentum spectrum sufficiently close to the lower boundary of the multi-particle spectrum. These states essentially encompass those states in the multi-particle spectrum lying below the three-particle threshold. In our proof, we draw on insights from proofs of asymptotic completeness in quantum mechanics. Notably, we apply Mourre’s conjugate operator method for the first time within the framework of Haag–Kastler quantum field theory. Furthermore, we discuss applications of our findings for the problem of asymptotic completeness in local relativistic quantum field theory.

Local Magnetic Field Inversion Based on the Modified L-Curve Model Function Method

Local Magnetic Field Inversion Based on the Modified L-Curve Model Function Method

Biomimetic Wings for Micro Air Vehicles.

In this work, micro air vehicles (MAVs) equipped with bio-inspired wings are investigated experimentally in wind tunnel. The starting point is that insects such as dragonflies, butterflies and locusts have wings with rigid tubular elements (corrugation) connected by flexible parts (profiling). So far, it is important to understand the specific aerodynamic effects of corrugation and profiling as applied to conventional wings for the optimization of low-Reynolds-number aerodynamics. The present study, in comparison to previous investigations on the topic, considers whole MAVs rather than isolated wings. A planform with a low aperture-to-chord ratio is employed in order to investigate the interaction between large tip vortices and the flow over the wing surface at large angles of incidence. Comparisons are made by measuring global aerodynamic loads using force balance, specifically drag and lift, and detailed local velocity fields over wing surfaces, by means of particle image velocimetry (PIV). This type of combined global-local investigation allows describing and relating overall MAV performance to detailed high-resolution flow fields. The results indicate that the combination of wing corrugation and profiling gives effective enhancements in performance, around 50%, in comparison to the classical flat-plate configuration. These results are particularly relevant in the framework of low-aspect-ratio MAVs, undergoing beneficial interactions between tip vortices and large-scale separation.

A thermophysical mechanism exploration of the brain: Motor cortex modeling with canonical ensemble theory

The brain, recognized as one of the most intricate systems globally, has been a focal point for scientific exploration. Researchers have made efforts to construct models of the brain based on neural dynamics and complex networks to gain insights into its workings. It is crucial to investigate the brain's working principles from various perspectives. This study presents a novel thermophysical model of the motor cortex and examines its potential thermodynamic properties. Utilizing canonical ensemble theory, we constructed the thermophysical model using spike and local field potential (LFP) signals obtained from intracortical brain-machine-interfaces (iBMIs) in two monkeys. The parameters derived from this model—namely internal energy, free energy, and entropy—were employed to assess the thermodynamic properties and observe alterations in these properties during reaching and grasping movements. Furthermore, this proposed model was applied to movement pattern decoding, highlighting its potential in neural decoding tasks. In both LFP- and spike-based thermodynamic models, there was an increase in internal energy and free energy, coupled with a decrease in entropy when the motor cortex was activated across various movement tasks. This suggests that the neural system adheres to the principles of a thermophysical system. Notably, the thermodynamic features demonstrated superior performance in decoding movement intentions compared to traditional LFP and spike features. This study represents the first construction of a comprehensive thermodynamic model of the motor cortex based on LFP and spike signals. The model exhibits remarkable stationarity and holds promise for long-term and stable evaluations of motor cortex functions.

Multi-receptor skin with highly sensitive tele-perception somatosensory.

The limitations and complexity of traditional noncontact sensors in terms of sensitivity and threshold settings pose great challenges to extend the traditional five human senses. Here, we propose tele-perception to enhance human perception and cognition beyond these conventional noncontact sensors. Our bionic multi-receptor skin employs structured doping of inorganic nanoparticles to enhance the local electric field, coupled with advanced deep learning algorithms, achieving a ΔV/Δd sensitivity of 14.2, surpassing benchmarks. This enables precise remote control of surveillance systems and robotic manipulators. Our long short-term memory-based adaptive pulse identification achieves 99.56% accuracy in material identification with accelerated processing speeds. In addition, we demonstrate the feasibility of using a two-dimensional (2D) sensor matrix to integrate real object scan data into a convolutional neural network to accurately discriminate the shape and material of 3D objects. This promises transformative advances in human-computer interaction and neuromorphic computing.

Manipulating the interfacial water structure by electron redistribution for the hydrogen evolution reaction.

The sluggish dissociation of water in alkaline electrolytes significantly hinders the kinetics of the hydrogen evolution reaction (HER), particularly on surfaces of Ru-based catalysts. The structure of water at the water-catalyst interface influences this dissociation process, yet controlling the configuration of water molecules is challenging due to their random distribution. In this study, a NiRu alloy supported on nitrogen-doped carbon (NiRu/NC) is selected as a model catalyst to investigate the electron distribution of the catalyst manipulating the adsorption configuration and orientation of water molecules. The introduction of Ni leads to charge transfer from Ni to Ru atoms within the NiRu alloy, causing a notable redistribution of charge that strengthens the local electric fields surrounding the NiRu alloy. These electron-rich Ru sites attract K+ cations to the surface, resulting in an increased presence of K+ cation-hydrated water molecules, which is an H-down configuration with a reduced Ru-H distance. This phenomenon is confirmed by in situ Raman spectroscopy. Consequently, NiRu/NC exhibits outstanding HER performance, achieving low overpotentials of 16 and 344 mV at current densities of 10 and 1000 mA cm-2, respectively.