Local Field Research Articles

The Gaps Between Institutional and Practical Disaster Risk Management Measures on Coastal Flood Risks in South Korea’s Coastal Communities

Global warming and climate change significantly increase the frequency of coastal floods caused by sea level rise (SLR) as a permanent factor and hydrometeorological hazards as tentative factors. The combined risks will affect coastal communities. South Korea is gradually facing SLR risks, mainly in its southern coastal regions; however, disaster risk reduction (DRR) in coastal regions remains fragmented. This study aimed to investigate the status of DRR for coastal communities in South Korea by looking at government practices and testimonies from residents. This study reviewed DRR-related regulations and documents and collected data from interviews with local government officials, field visits, and informal conversations with residents in six coastal communities. The findings show that the coastal communities were less resilient to coastal floods than to other hazards, such as typhoons and heavy rains, and the potential consequences could be expanded due to demographic challenges, fragmented institutional systems, and low risk awareness. Moreover, this study emphasized the necessity of an integrated approach to reducing the impact of coastal floods caused by both SLR and other factors. It also highlighted the importance of coastal community engagement in local DRR activities through increasing risk awareness and adapting to environmental change based on appropriate risk information disclosure by the government. The impacts of coastal floods triggered by SLR and other hazard factors can be reduced by aligning practical regulatory measures with adaptive strategies and enhancing the disaster resilience of coastal communities.

Turbulence Embedded Into the Ionosphere by Electromagnetic Waves

AbstractWhen charged particles are accelerated from Earth's magnetosphere and precipitate into the atmosphere, their impact with neutral gas creates the aurora. Structured electric fields drive the acceleration processes but they are also passed down to the ionosphere, meaning that turbulence can in part be embedded into the ionosphere rather than emerge through instability processes locally. Applying a point‐cloud analysis technique adapted from observational cosmology, we show how observed turbulence in the ionosphere matches electrical current signatures in the pulsating aurora in a series of conjunctions between space‐ and ground‐based instruments. We propose that the temporal spectrum of pulsations in the pulsating aurora is the driver of a clearly observed energy injection into the ionosphere's unstable bottomside. Precipitating electrons produce electric fields through charge deposition, and we observe wave characteristics that are present in this pattern. Next, the relative electron‐ion drifts excite the Farley‐Buneman instability, the distribution of whose waves are organized according to the local electric field. It is the temporal characteristics of chorus wave interactions in the magnetosphere that is imparted, via precipitating electrons, to the pulsating aurora, and so we propose that chorus wave interactions are capable of embedding turbulent structure into the ionosphere. This structure (now pressure gradients) dissipate energy in the E‐region through turbulent processes, observed by the icebear coherent scatter radar.

Very local impact on the spectrum of cosmic-ray nuclei below 100 TeV

Recent measurements of primary and secondary CR spectra, their arrival directions, and our improved knowledge of the magnetic field geometry around the heliosphere allow us to set a bound on the distance beyond which a puzzling 10-TeV “bump” and certain related spectral features cannot originate. The sharpness of the spectral breaks associated with the bump, the abrupt change of the CR intensity across the local magnetic equator (90° pitch angle), and the similarity between the primary and secondary CR spectral patterns point to a local reacceleration of the bump particles out of the background CRs. We argue that, owing to a steep preexisting CR spectrum, a nearby shock may generate such a bump by boosting particle rigidity by a mere factor of ∼1.5 in the range below 50 TV. Reaccelerated particles below ∼0.5 TV are convected with the interstellar medium flow and do not reach the Sun. The particles above this rigidity then form the bump. This single universal process is responsible for the observed spectral features of all CR nuclei, primary and secondary, in the rigidity range below 100 TV. We propose that one viable candidate is the system of shocks associated with ∊ Eridani star at 3.2 pc of the Sun, which is well aligned with the direction of the local magnetic field. Other shocks, such as old supernova shells, may produce a similar effect. We provide a simple formula that reproduces the spectra of all CR species with only three parameters uniquely derived from the CR proton data. We show how our formalism predicts helium, boron, carbon, oxygen, and iron spectra, for which accurate data in GV-TV range exist. Our model thus unifies all the CR spectral features observed below 50 TV.

Interaction of inertia and magnetic force in the liquid metal flow past a magnetic obstacle

In this paper, we present a numerical study of the three-dimensional behavior of a liquid metal flow in an insulating rectangular duct of narrow cross section past a localized magnetic field (i.e., a magnetic obstacle) produced by two parallel square magnets arranged externally on the walls of the duct. A series of simulations are conducted focused mainly on describing the interplay between inertial and magnetic forces in a wide range of interaction parameters (1.8<N<48) by varying the Reynolds number while the Hartmann number is kept fixed (Ha = 75). The analyzed configuration coincides with that studied experimentally by Domínguez et al. [“Experimental and theoretical study of the dynamics of wakes generated by magnetic obstacles,” Magnetohydrodynamics 51(2), 215–224 (2015)] and, as a first step, experimental data from local variables (streamwise velocity component) and global parameters (oscillation frequency and kinetic energy of the wake) are consistently replicated by the numerical model. Furthermore, to complement the flow phenomenology, the transition to different flow structures as the interaction parameter varies is explored. It is found that when the magnetic forces predominate over inertia, stationary vortex patterns with two, four, and six vortices appear while, unlike the hydrodynamic flow past a bluff body, the increase in inertial effects leads to a reduction in the number of vortices and eventually to their disappearance, reaching a state in which the magnetic obstacle becomes imperceptible to the flow. The existence of a critical value of the interaction parameter that maximizes the kinetic energy of the wake is confirmed numerically and corroborated from the experimental data.

Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics-Informed Neural Networks.

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full-field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data-driven models for learning full-field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full-field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics-informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics-informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo-Hookean, Mooney Rivlin, and Gent. The improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the trainingdata.

Equivalent Definitions of Arthur Packets for Real Classical Groups

Arthur has conjectured the existence of what are now known as Arthur packets of representations of reductive algebraic groups over local and global fields. In the case of special orthogonal and symplectic groups, he subsequently gave a definition of these packets, using local and global methods. For general real groups, an alternative approach to the definition of Arthur packets has been given by Adams-Barbasch-Vogan. This construction is purely local and uses geometric methods. Our main result is that these two definitions agree in the case of real special orthogonal and symplectic groups.

Numerical evaluation for the peristaltic flow in the proximity of double-diffusive convection of non-Newtonian nanofluid under the MHD

This article mainly studies the 2-D propagation of a non-compressible Eyring-Powell nanofluid flow through a stretched wedge under the Magneto-hydrodynamic effect. Equations for temperature, concentration, double-diffusive convection and momentum are taken into consideration. Since solving the dimensionless equations associated with our study is an uphill task, we utilize the MATLAB bvp4c solver to illustrate the graphical performance of different parameters. This manuscript may be significant in the projects in the field of industry and medicine. The manuscript's noteworthy features include the magnetic field, heat source-sink parameter, double diffusivity, and solar radiation process. The main finding is that the local fluid parameter k1 and magnetic field parameter M decelerate the velocity of nanofluid. Because different nanoparticles have different effects on fluids, the fluid's temperature exhibits multiple behaviors, therefore by escalating the Prandtl number initially, it increases and then decelerates due to the presence of nanoparticles. The concentration of fluid declines as the Schmidt number rises. The double diffusivity of Eyring-Powell nanofluid improves with magnification in the fluid's Schmidt number Sc and Prandtl number Pr.

Effects of aggregate's type and orientation on stress concentration and crack propagation of modeled concrete applied a shear force

This study focused on planarizing natural modeled aggregate concrete (NMC), recycled modeled aggregate concrete (RMC), and brick modeled aggregate concrete (BMC) for analyzing the influences of aggregates on shearing failure mechanism. A designed apparatus was used to load a shear force for the specimens and analyzed the global and local strain fields near the interface transition zone (ITZ) using digital image correlation (DIC) analysis. Additionally, we employed COMSOL software simulation to understand the crack initiation process and damage evolution mechanism of related concrete. Our findings revealed that the brick aggregate leaded to the lowest modulus of elasticity and compressive strength, resulting in poor shear resistance. However, the recycled aggregate model exhibited the highest shear resistance, primarily due to the old mortar acting as a buffer layer in the ITZ. During loading, the stress in the NMC model concentrated in the load transfer zone and gradually moved to the left and right sides of the built-in aggregate model until fracture, as observed through DIC analysis. The stress distribution of the RMC was similar with those of the NMC, but the BMC showed a distinct stress distribution pattern. Furthermore, the corner of the built-in modeled aggregate significantly influenced the crack propagating path and stress distribution. Therefore, the modeled aggregate corner plays a crucial role in the shear behavior of concrete, affecting both crack propagation and overall mechanical performance. The practical implications of our study can inform the design and application of recycled concrete.

An Infrared Nanospectroscopy Technique for the Study of Electric-Field-Induced Molecular Dynamics.

Static electric fields play a considerable role in a variety of molecular nanosystems as diverse as single-molecule junctions, molecules supporting electrostatic catalysis, and biological cell membranes incorporating proteins. External electric fields can be applied to nanoscale samples with a conductive atomic force microscopy (AFM) probe in contact mode, but typically, no structural information is retrieved. Here we combine photothermal expansion infrared (IR) nanospectroscopy with electrostatic AFM probes to measure nanometric volumes where the IR field enhancement and the static electric field overlap spatially. We leverage the vibrational Stark effect in the polymer poly(methyl methacrylate) for calibrating the local electric field strength. In the relevant case of membrane protein bacteriorhodopsin, we observe electric-field-induced changes of the protein backbone conformation and residue protonation state. The proposed technique also has the potential to measure DC currents and IR spectra simultaneously, insofar enabling the monitoring of the possible interplay between charge transport and other effects.

Diffusosphere engineering in BNT-based multilayer heterogeneous film capacitors for high performance

Combining layers with high breakdown resistance and high polarization is a promising approach for designing dielectric capacitors with high energy density and efficiency. However, such combinations often accompany strong interfacial polarization, magnification of local electric fields, leading to premature breakdown. This work addresses this issue via controlled formation of diffusospheres. We constructed multilayer heterogeneous films using two Bi0.5Na0.5TiO3 (BNT)-based substances with high breakdown resistance and high polarization properties. Experimental results and finite element simulations demonstrate that the energy storage capacity of these films effectively harnesses the advantages of both phases. Notably, the interface polarization is minimal. Instead, a solid solution-like diffusosphere, formed by the mutual diffusion of ions between the two phases, plays a crucial role. The diffusosphere acts as a transition zone, mitigating charge aggregation at the interfaces and optimizing the relaxor and breakdown characteristics of the capacitor. With six diffusospheres, the multilayer heterogeneous capacitor achieves a recoverable energy storage density of 94 J/cm3, a significant advancement in BNT-based energy storage films. This work proposes and validates the concept of diffusospheres and their role in reducing interfacial polarization in multilayer heterogeneous films, enhancing the understanding of heterogeneous composite structures and advancing the field of dielectric energy storage.

Design and initial results of the imaging neutral particle analyzer in large helical device.

A novel Imaging Neutral Particle Analyzer (INPA) was newly installed in early 2024 to enhance the understanding of fast ion confinement on Large Helical Devices (LHDs). This diagnostic system, based on a magnetic spectrometer using a scintillator, provides energy-resolved radial profiles of confined fast ions by measuring charge-exchanged fast neutrals escaping from the plasma. The system utilizes a 100nm thick carbon foil to ionize the fast neutrals, subsequently deflecting the ions toward a scintillator via the existing local magnetic field. The fast ion energy and sightline determine the position of the scintillation, while the light intensity depends on the flux of the fast ions. The INPA features two apertures, facilitating effective measurements in both clockwise and counterclockwise magnetic field directions in the LHD. This INPA was designed as a passive measurement system that measures fast ions charge exchange with background neutrals, focusing on perpendicular beam ions from 5 to 100keV with an energy resolution of about 5.75keV. This paper describes the details of the design, installation, and the initial results of the INPA on the LHD. This work will contribute significantly to a deeper understanding of fast ion transport due to magnetohydrodynamic instabilities.

Quantum delocalization, structural order, and density response of the strongly coupled electron liquid

We investigate the impact of electronic correlations and quantum delocalization onto the static structure factor and static density response function of the strongly coupled electron liquid. In contrast to a classical system, the density response of the electron liquid vanishes on small length scales due to quantum delocalization effects, which we rigorously quantify in terms of imaginary-time correlation functions and dynamic Matsubara response functions. This allows us to analyze the interplay of structural order and dynamic quantum effects as it manifests itself in the dynamic Matsubara local field correction. Finally, we identify an effective electronic attraction in the spin-offdiagonal static density response when the wavelength of the perturbation is commensurate with the average interparticle distance.

A 0.00179 mm2/Ch Chopper-Stabilized TDMA Neural Recording System with Dynamic EOV Cancellation and Predictive Mixed-Signal Impedance Boosting.

This article presents a digitally-assisted multi-channel neural recording system. The system uses a 16-channel chopper-stabilized Time Division Multiple Access (TDMA) scheme to record multiplexed neural signals into a single shared analog front end (AFE). The choppers reduce the total integrated noise across the modulated spectrum by 2.4× and 4.3× in Local Field Potential (LFP) and Action Potential (AP) bands, respectively. In addition, a novel impedance booster based on Sign-Sign least mean squares (LMS) adaptive filter (AF) predicts the input signal and pre-charges the AC-coupling capacitors. The impedance booster module increases the AFE input impedance by a factor of 39× with a 7.1% increase in area. The proposed system obviates the need for on-chip digital demodulation, filtering, and remodulation normally required to extract Electrode Offset Voltages (EOV) from multiplexed neural signals, thereby achieving 3.6× and 2.8× savings in both area and power, respectively, in the EOV filter module. The Sign-Sign LMS AF is reused to determine the system loop gain, which relaxes the feedback DAC accuracy requirements and saves 10.1× in power compared to conventional oversampled DAC truncation-error ΔΣ-modulator. The proposed SoC is designed and fabricated in 65 nm CMOS, and each channel occupies 0.00179 mm2 of active area. Each channel consumes 5.11 μW of power while achieving 2.19 μVrms and 2.4 μVrms of input referred noise (IRN) over AP and LFP bands. The resulting AP band noise efficiency factor (NEF) is 1.8. The proposed system is verified with acute in-vivo recordings in a Sprague-Dawley rat using parylene C based thin-film platinum nanorod microelectrodes.

Serotonin transporter knockout in rats reduces beta- and gamma-band functional connectivity between the orbitofrontal cortex and amygdala during auditory discrimination.

The orbitofrontal cortex and amygdala collaborate in outcome-guided decision-making through reciprocal projections. While serotonin transporter knockout (SERT-/-) rodents show changes in outcome-guided decision-making, and in orbitofrontal cortex and amygdala neuronal activity, it remains unclear whether SERT genotype modulates orbitofrontal cortex-amygdala synchronization. We trained SERT-/- and SERT+/+ male rats to execute a task requiring to discriminate between two auditory stimuli, one predictive of a reward (CS+) and the other not (CS-), by responding through nose pokes in opposite-side ports. Overall, task acquisition was not influenced by genotype. Next, we simultaneously recorded local field potentials in the orbitofrontal cortex and amygdala of both hemispheres while the rats performed the task. Behaviorally, SERT-/- rats showed a nonsignificant trend for more accurate responses to the CS-. Electrophysiologically, orbitofrontal cortex-amygdala synchronization in the beta and gamma frequency bands during response selection was significantly reduced and associated with decreased hubness and clustering coefficient in both regions in SERT-/- rats compared to SERT+/+ rats. Conversely, theta synchronization at the time of behavioral response in the port associated with reward was similar in both genotypes. Together, our findings reveal the modulation by SERT genotype of the orbitofrontal cortex-amygdala functional connectivity during an auditory discrimination task.

A novel hybrid Wireless Integrated Sensing Detector for simultaneous EEG and MRI (WISDEM).

Concurrent recording of EEG/fMRI signals reveals cross-scale neurovascular dynamics that are crucial for elucidating fundamental linkage between function and behaviors. However, MRI scanners generate tremendous artifacts for EEG detection. Despite existing denoising methods, cabled connections to EEG receivers are susceptible to environmental fluctuations inside MRI scanners, creating baseline drifts that complicate EEG signal retrieval from the noisy background. Here, a Wireless Integrated Sensing Detector for simultaneous EEG and MRI (WISDEM) is developed to encode fMRI and EEG signals on distinct sidebands of the detector oscillation carrier wave for detection by a standard MRI console over the entire duration of fMRI sequence. Local field potential (LFP) and fMRI maps are retrieved through low-pass and high-pass filtering of frequency-demodulate signals. From optogenetically-stimulated somatosensory cortex, the positive correlation between evoked LFP and fMRI signals validates strong neurovascular coupling, enabling cross-scale brain mapping with this 2-in-1 transducer as a research and diagnostic tool.

Ferroelectric Al0.85Sc0.15N and Hf0.5Zr0.5O2 Domain Switching Dynamics.

The capability to reliably program partial polarization states with nanosecond programming speed and femtojoule energies per bit in ferroelectrics makes them an ideal candidate to realize multibit memory elements for high-density crossbar arrays, which could enable neural network models with a large number of parameters at the edge. However, a thorough understanding of the domain switching dynamics involved in the polarization reversal is required to achieve full control of the multibit capability. Transient current integration measurements are adopted to investigate the domain dynamics in aluminum scandium nitride (Al0.85Sc0.15N) and hafnium zirconium oxide (Hf0.5Zr0.5O2). The switching dynamics are correlated to the crystal structure of the films. The contributions of domain nucleation and domain wall motion are decoupled by analyzing the rate of change of the time-dependent normalized switched polarization. Thermally activated creep domain wall motion characterizes the Al0.85Sc0.15N switching dynamics. The statistics of independently nucleating domains and the domain wall creep motion in Hf0.5Zr0.5O2 are associated with the spatially inhomogeneous distribution of local switching field due to polymorphism, absence of preferential crystallite orientation, as well as defects and charges that can be located at the grain boundaries. The c-axis texture, single-phase nature, and strong likelihood of less fabrication process-induced defects contribute to the homogeneity of the local switching field in Al0.85Sc0.15N. Nonetheless, defects generated and redistributed upon bipolar electric field switching cycling result in Al0.85Sc0.15N domain wall pinning. The wake-up effect in Hf0.5Zr0.5O2 is explained thorough the continuous addition of switchable regions associated with two independent distributions of characteristic switching times.

An advanced 3D DNA nanoplatform for spatiotemporally confined enhanced dual-mode biosensing MicroRNA in cancer cell

Dual-mode signal output platforms have demonstrated considerable promise due to their improved anti-interference capability and inherent signal self-correction. Nevertheless, traditional discrete-distributed signal probes often encounter significant drawbacks, including limited mass transfer efficiency, diminished signal strength, and instability in intricate biochemical environments. In response to these challenges, a scalable and hyper-compacted 3D DNA nanoplatform resembling “periodic focusing heliostat” has been developed for synergistically enhanced fluorescence (FL) and surface-enhanced Raman spectroscopy (SERS) biosensing of miRNA in cancer cells. Our approach utilized a distinctive assembly strategy integrating gold nanostars (GNS) as fundamental “heliostat units” linked by palindromic DNA sequences to facilitate each other hand-in-hand cascade alignment and condensed into large scale nanostructures. This configuration was further augmented by the incorporation of gold nanoparticles (GNP) via strong Au–S bonds, resulting in a sturdy framework for improved signal transduction. The initiation of this assembly process was mediated by the hybridization of dsDNA to miRNA-21, which served as a primer for polymerization and nicking reactions, thus generating a multifunctional T2 probe. This probe is intricately designed with three distinct parts: a 3′-palindromic end for structural integrity, a central region for capturing SERS-active probes (Cy3-P2), and a 5′-segment for attaching fluorescence reporters. Upon integration T2 into the GNS-based heliostat unit, it promotes palindromic arm-induced aggregation and plasma exciton coupling between plasma nanoparticles and signal transduction tags. This clustered arrangement creates a high-density “hot spot” array that maximizes the local electromagnetic fields necessary for enhanced SERS and FL response. This superstructure supports enhanced aggregation-induced signal amplification for both SERS and FL, offering exceptional sensitivity with LOD as low as 0.0306 pM and 0.409 pM. The efficacy of this method was demonstrated in the evaluation of miRNA-21 in various cancer cell lines.

Local Electric Field Accelerates Zn2+ Diffusion Kinetics for Zn‐V Battery

AbstractVanadium‐based aqueous zinc‐ion batteries (AZIBs) exhibit significant potential for large‐scale energy storage applications, attributed to their inherent safety characteristics. Addressing the slow transport kinetics of divalent Zn2+ within the cathode lattice, thereby enhancing the rate capability and stability, is essential for the Zn‐V battery system. In this study, a local electric field (LEF) strategy is introduced to accelerate the Zn2+ diffusion by creating abundant oxygen vacancies (Ov) in V2O5. Comprehensive characterization and density functional theory (DFT) calculations reveal the formation of the Ov induced atomic‐level donor‐acceptor couple configuration, verify and visualize the LEF. The fabricated LEF‐enhanced vanadium oxide (LEF‐VO) exhibits exceptional rate capability, achieving 338.3 mA h g−1 at a current density of 10 A g−1, and maintaining 66.4% of its capacity over a range from 0.2 to 20 A g−1. Furthermore, the influence of the LEF on expediting Zn2+ diffusion kinetics is elucidated, correlating to the electrical force. This novel LEF approach offers valuable insights for advancing high‐rate cathode materials.

Multitask learning of a biophysically-detailed neuron model.

The human brain operates at multiple levels, from molecules to circuits, and understanding these complex processes requires integrated research efforts. Simulating biophysically-detailed neuron models is a computationally expensive but effective method for studying local neural circuits. Recent innovations have shown that artificial neural networks (ANNs) can accurately predict the behavior of these detailed models in terms of spikes, electrical potentials, and optical readouts. While these methods have the potential to accelerate large network simulations by several orders of magnitude compared to conventional differential equation based modelling, they currently only predict voltage outputs for the soma or a select few neuron compartments. Our novel approach, based on enhanced state-of-the-art architectures for multitask learning (MTL), allows for the simultaneous prediction of membrane potentials in each compartment of a neuron model, at a speed of up to two orders of magnitude faster than classical simulation methods. By predicting all membrane potentials together, our approach not only allows for comparison of model output with a wider range of experimental recordings (patch-electrode, voltage-sensitive dye imaging), it also provides the first stepping stone towards predicting local field potentials (LFPs), electroencephalogram (EEG) signals, and magnetoencephalography (MEG) signals from ANN-based simulations. While LFP and EEG are an important downstream application, the main focus of this paper lies in predicting dendritic voltages within each compartment to capture the entire electrophysiology of a biophysically-detailed neuron model. It further presents a challenging benchmark for MTL architectures due to the large amount of data involved, the presence of correlations between neighbouring compartments, and the non-Gaussian distribution of membrane potentials.

Spatial transcriptomics at the brain-electrode interface in rat motor cortex and the relationship to recording quality

Study of the foreign body reaction to implanted electrodes in the brain is an important area of research for the future development of neuroprostheses and experimental electrophysiology. After electrode implantation in the brain, microglial activation, reactive astrogliosis, and neuronal cell death create an environment immediately surrounding the electrode that is significantly altered from its homeostatic state. Objective. To uncover physiological changes potentially affecting device function and longevity, spatial transcriptomics (ST) was implemented to identify changes in gene expression driven by electrode implantation and compare this differential gene expression to traditional metrics of glial reactivity, neuronal loss, and electrophysiological recording quality. Approach. For these experiments, rats were chronically implanted with functional Michigan-style microelectrode arrays, from which electrophysiological recordings (multi-unit activity, local field potential) were taken over a six-week time course. Brain tissue cryosections surrounding each electrode were then mounted for ST processing. The tissue was immunolabeled for neurons and astrocytes, which provided both a spatial reference for ST and a quantitative measure of glial fibrillary acidic protein and neuronal nuclei immunolabeling surrounding each implant. Main results. Results from rat motor cortex within 300 µm of the implanted electrodes at 24 h, 1 week, and 6 weeks post-implantation showed up to 553 significantly differentially expressed (DE) genes between implanted and non-implanted tissue sections. Regression on the significant DE genes identified the 6–7 genes that had the strongest relationship to histological and electrophysiological metrics, revealing potential candidate biomarkers of recording quality and the tissue response to implanted electrodes. Significance. Our analysis has shed new light onto the potential mechanisms involved in the tissue response to implanted electrodes while generating hypotheses regarding potential biomarkers related to recorded signal quality. A new approach has been developed to understand the tissue response to electrodes implanted in the brain using genes identified through transcriptomics, and to screen those results for potential relationships with functional outcomes.