In this work, we investigate hypothermic temperature effects on whole human blood using electrical impedance spectroscopy (EIS), with frequency response measurements spanning 100 Hz to 10 MHz. Measurements are conducted within a microfluidic system designed to mimic physiologically relevant blood vessels. Hypothermia remains a persistent challenge in medicine, as it alters physiological responses and can cause traditional medical sensors to produce inaccurate or unreliable measurements. Historically, EIS has provided a sensitive method for probing the electrochemical and dielectric properties of blood components independently across a range of temperature and flow conditions. This research aims to compound the effects of temperature and flow to enable more precise electrical characterizations relevant to hypothermic states.

Electroceutical implants that deliver targeted neural stimulation have shown therapeutic potential for a wide range of neurological and peripheral disorders, yet wirelessly powering ultra-miniaturized, fully injectable systems remains a critical challenge. Here we report a Thread-like Injectable Neural TechnologY (TINY), powered via a differential tissue-coupled powering (DTCP) scheme. DTCP employs mid-frequency differential potentials applied across external electrodes on a compact, wearable transmitter to deliver energy through tissue to an ultra-miniaturized, thread-like implant that integrates a custom ASIC and PEDOT-coated receiver and stimulation electrodes. Benchtop experiments in agar phantoms characterize the power-transfer efficiency (PTE) and reveal that PTE increases with implant length while maintaining strong tolerance to angular misalignment. In vivo tests in rat hindlimbs further demonstrate wireless activation of the sciatic nerve through tissue at centimeter-scale depths, confirming effective transcutaneous energy delivery for neurostimulation. A 20-day implantation study shows stable positioning of the device with minimal tissue response, indicating excellent chronic compatibility. These findings address long-standing challenges in wirelessly powering injectable electroceuticals and establish DTCP as a scalable and alignment-robust powering strategy for future minimally invasive neuromodulation therapies.

Wideband gap devices present several challenges to device simulation. First, the minority carrier population is small and presents challenges to precision in the calculations. Second, the negative velocity field relationship due to carriers scattering into an upper band with higher mass can create convergence issues. Third, electron and hole traps can be important to the response and are often implemented as a delta function in energy. This can create convergence issues when small changes in the Fermi level create large changes in occupancy. This is made harder when, as in standard Scharfetter-Gummel, the solution variable is concentration. The Fermi level has to be derived in a complicated way from the concentration. Fourth, in many cases these devices are used in power applications and the break down voltage is a critical figure of merit. Break down is difficult to simulate since it is an abrupt change in current flow with a small change in applied voltage. Current collapse provides a similar challenge in simulation. Approaches to these issues will be discussed and demonstrated.

High Electron Mobility Transistors (HEMTs) are widely used in aerospace systems for their high efficiency and inherent radiation tolerance, yet heavy ion strikes can still induce significant damage and performance degradation. This work investigates the origin of current collapse observed in AlGaN/GaN HEMTs exposed to 8.4 MeV Bi ions over a range of fluences. The hypothesis is that heavy-ion damage creates a localized region of neutral electron traps near the gate–drain edge, coinciding with the device’s highest electric-field stress. These traps capture electrons and form a persistent negative charge distribution that depletes the 2DEG channel, suppressing drain current. DC-biased irradiation measurements confirm this behavior, showing increasing collapse with increasing fluence and field strength. A TCAD model calibrated to non-irradiated devices reproduces this mechanism and provides a quantitative explanation for the observed current collapse phenomenon.

Electroceutrical implants that deliver targeted neural stimulation show strong therapeutic promise, yet wirelessly powering ultra-miniaturized, fully injectable systems remains challenging. Here we present a thread-like injectable neural technology (TINY) powered by a differential tissue-coupled powering (DTCP) scheme. DTCP applies mid-frequency differential potentials across external electrodes on a compact wearable transmitter to deliver energy through tissue to an implant integrating a custom ASIC and PEDOT-coated electrodes. Benchtop studies in agar phantoms reveal that DTCP’s power-transfer efficiency increases with implant length without a proportional increase in cross-section or overall volume, while maintaining high angular misalignment tolerance. In vivo tests in rat hindlimbs demonstrate wireless sciatic nerve activation, confirming effective transcutaneous energy delivery. A 20-day implantation study further shows stable positioning and minimal tissue response, indicating chronic compatibility. DTCP thus overcomes key limitations of conventional wireless powering and provides a scalable, alignment-robust strategy for minimally invasive electroceutical therapies.

Interfaces that can capture extensive motor-control information from nerves would be valuable for amputees to control state-of-the-art prosthetic limbs with precision and speed. This paper uses advanced modeling techniques to study the impact of electrode diameter, electrode spacing, axon diameter, and voltage threshold on the volume of recording around each electrode and the fraction of nodes that the interface can record. When designing a 3-D array of thin-film electrodes for a nerve interface, higher fractions of recorded nodes can be achieved if the electrode-spacing is not a cubic arrangement, but instead follows the shape aspect ratios defined by the individual volumes of recording. Specifically, when the longitudinal spacing is $\boldsymbol{\sim} \mathbf{2. 6 5}$ times the lateral or vertical spacing. Clinical Relevance—Impact the design of nerve interfaces designed for amputees to control advanced prostheses

We present a wireless, battery-free neurostimulation platform powered via Differential Tissue Coupled Powering (DTCP), enabling the use of high-frequency ($>\text{MHz}$) currents for sub- $\text{mm}^{3}$ implant miniaturization. Using four-port modeling and experimental validation, we analyze how power transfer efficiency (PTE) depends on transmitter and receiver spacing. We show that PTE can be significantly improved by increasing implant length alone, without altering width or thickness, which is amenable to minimally invasive injectables. We also demonstrate real-time, fully wireless power measurement in vitro via LED-based optical readout. Modeling confirms that thread-like geometry supports efficient axonal stimulation, and in vivo rat studies show stable implant positioning with minimal scarring. Together, these results establish DTCP as a scalable and minimally invasive powering method for next-generation injectable electroceuticals.

Deep brain stimulation (DBS) is a neuromodulatory therapy that has been FDA approved for the treatment of various disorders, including but not limited to, movement disorders (e.g., Parkinson’s disease and essential tremor), epilepsy, and obsessive-compulsive disorder. Computational methods for estimating the volume of tissue activated (VTA), coupled with brain imaging techniques, form the basis of models that are being generated from retrospective clinical studies for predicting DBS patient outcomes. For instance, VTA models are used to generate target-and network-based probabilistic stimulation maps that play a crucial role in predicting DBS treatment outcomes. This review defines the methods for calculation of tissue activation (or modulation) including ones that use heuristic and clinically derived estimates and more computationally involved ones that rely on finite-element methods and biophysical axon models. We define model parameters and provide a comparison of commercial, open-source, and academic simulation platforms available for integrated neuroimaging and neural activation prediction. In addition, we review clinical studies that use these modeling methods as a function of disease. By describing the tissue-activation modeling methods and highlighting their application in clinical studies, we provide the neural engineering and clinical neuromodulation communities with perspectives that may influence the adoption of modeling methods for future DBS studies.

Compound nerve action potentials (CNAPs) were used as a metric to assess the stimulation performance of a novel high-density, transverse, intrafascicular electrode in rat models. We show characteristic CNAPs recorded from distally implanted cuff electrodes. Evaluation of the CNAPs as a function of stimulus current and calculation of recruitment plots were used to obtain a qualitative approximation of the neural interface’s placement and orientation inside the nerve. This method avoids elaborate surgeries required for the implantation of EMG electrodes and thus minimizes surgical complications and may accelerate the healing process of the implanted subject.

Objective.Computational models are powerful tools that can enable the optimization of deep brain stimulation (DBS). To enhance the clinical practicality of these models, their computational expense and required technical expertise must be minimized. An important aspect of DBS models is the prediction of neural activation in response to electrical stimulation. Existing rapid predictors of activation simplify implementation and reduce prediction runtime, but at the expense of accuracy. We sought to address this issue by leveraging the speed and generalization abilities of artificial neural networks (ANNs) to create a novel predictor of neural fiber activation in response to DBS.Approach.We developed six variations of an ANN-based predictor to predict the response of individual, myelinated axons to extracellular electrical stimulation. ANNs were trained using datasets generated from a finite-element model of an implanted DBS system together with multi-compartment cable models of axons. We evaluated the ANN-based predictors using three white matter pathways derived from group-averaged connectome data within a patient-specific tissue conductivity field, comparing both predicted stimulus activation thresholds and pathway recruitment across a clinically relevant range of stimulus amplitudes and pulse widths.Main results.The top-performing ANN could predict the thresholds of axons with a mean absolute error (MAE) of 0.037 V, and pathway recruitment with an MAE of 0.079%, across all parameters. The ANNs reduced the time required to predict the thresholds of 288 axons by four to five orders of magnitude when compared to multi-compartment cable models.Significance.We demonstrated that ANNs can be fast, accurate, and robust predictors of neural activation in response to DBS.

Technology Computer Aided Design (TCAD) is being developed for superconductor electronics (SCE). The AlO $_{x}$ tunnel barriers used in SCE have thicknesses on the order of the surface roughness of sputtered niobium. Surface roughness is not included in a standard CMOS TCAD process simulator and is among the unique modules needed for SCE. An empirical model for surface roughness is developed for TCAD process simulators. This model is merged with the existing sputtering module and coupled with chemical mechanical polishing and aluminum oxidation to generate process-simulated structures of the Nb/Al-AlO $_{x}$ /Nb junction stack. Electrical simulations of these junctions are used to extract room temperature conductance. The inherent statistical variation of the proposed surface roughness model lends itself to large scale parallel simulation of thousands of junctions. Statistical simulations are performed and statistical analysis is given.

The use of metallic nanostructures in the fabrication of bioelectrodes (e.g., neural implants) is gaining attention nowadays. Nanostructures provide increased surface area that might benefit the performance of bioelectrodes. However, there is a need for comprehensive studies that assess electrochemical performance of nanostructured surfaces in physiological and relevant working conditions. Here, we introduce a versatile scalable fabrication method based on magnetron sputtering to develop analogous metallic nanocolumnar structures (NCs) and thin films (TFs) from Ti, Au, and Pt. We show that NCs contribute significantly to reduce the impedance of metallic surfaces. Charge storage capacity of Pt NCs is remarkably higher than that of Pt TFs and that of the other metals in both morphologies. Circuit simulations of the electrode/electrolyte interface show that the signal delivered in voltage-controlled systems is less filtered when nanocolumns are used. In a current-controlled system, simulation shows that NCs provide safer stimulation conditions compared to TFs. We have assessed the durability of NCs and TFs for potential use in vivo by reactive accelerated aging test, mimicking one-year in vivo implantation. Although each metal/morphology reveals a unique response to aging, NCs show overall more stable electrochemical properties compared to TFs in spite of their porous structure.

By modeling the propagation of a seed layer with various crystal orientations, this study explores the influence of process variations on grain formation with the help of a physics-based process simulator. Grain boundaries allow easy diffusion of foreign atoms through the lattice, which causes Al to move inside the Nb bottom electrode layer and more interestingly, O to penetrate through the Al layer during oxidation and create a barrier with non-uniform thickness. In addition to thickness variations, the grain structure exhibited by Nb and Al can cause significant suppression of supercurrent at the boundaries depending on the degree of lattice mismatch, impurity deposition, etc. This work details the process simulation of grain boundary formation and aims to provide geometrical models that may be used in the simulation of device performance to account for process-induced variations.

Efforts to reduce the size of implanted microelectrodes and their supporting dielectric material are being driven by demands to reduce the foreign-body tissue response and increase spatial resolution. Although the impact on recording performance of electrode size and shape have been analyzed in both prior and on-going work, the impact of the width of the surrounding dielectric has not. In this work we use the FEM-reciprocity method, informed by NEURON, to examine the impact of dielectric widths, ranging from 1 to 256 mu m, on the range and shape of the recording volume around 8-mu m-diameter disc electrodes located inside a 700-mu m-diameter fascicle. We observe that reducing the width of the dielectric not only can reduce the recording range by up to 1/3 but also changes the region of tissue recorded from only above the electrode to equally above and below the electrode. As the width of the dielectric is scaled down, these observed impacts occur at different levels for different diameters fibers.

Exogenous electrical fields have been explored in regenerative medicine to increase cellular expression of pro-regenerative growth factors. Adipose-derived stem cells (ASCs) are attractive for regenerative applications, specifically for neural repair. Little is known about the relationship between low-level electrical stimulation (ES) and ASC regenerative potentiation. In this work, patterns of ASC expression and secretion of growth factors (i.e., secretome) were explored across a range of ES parameters. ASCs were stimulated with low-level stimulation (20 mV/mm) at varied pulse frequencies, durations, and with alternating versus direct current. Frequency and duration had the most significant effects on growth factor expression. While a range of stimulation frequencies (1, 20, 1000 Hz) applied intermittently (1 h × 3 days) induced upregulation of general wound healing factors, neural-specific factors were only increased at 1 Hz. Moreover, the most optimal expression of neural growth factors was achieved when ASCs were exposed to 1 Hz pulses continuously for 24 h. In evaluation of secretome, apparent inconsistencies were observed across biological replications. Nonetheless, ASC secretome (from 1 Hz, 24 h ES) caused significant increase in neurite extension compared to non-stimulated control. Overall, ASCs are sensitive to ES parameters at low field strengths, notably pulse frequency and stimulation duration.

It has been hypothesized that the variation of the critical currents in Nb/Al-AlOx/Nb junctions is due to, among other effects, the presence of grain boundaries in the system. Motivated by this, we examine the effect of grain boundaries on the critical current of a Josephson junction. We assume that the hopping amplitudes are dependent on the interatomic distance and derive a physically realistic model of distance-dependent hopping amplitudes. We find that the presence of a grain boundary and associated disorder is responsible for a very large drop in the critical current relative to a clean system. We also find that when a tunnel barrier is present, grain boundaries cause substantial variations in the critical currents due to the disordered hoppings near the tunnel barrier. We discuss the applicability of these results to Josephson junctions presently intended for use in superconducting electronics applications.

Movement DisordersVolume 36, Issue 3 p. 610-610 Hot Topics Moving From Wired to Wireless Brain Stimulation to Treat Movement Disorders: Are We Breaking Ground? Aparna Wagle Shukla MD, Corresponding Author Aparna Wagle Shukla MD aparna.shukla@neurology.ufl.edu Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, Florida, USA Correspondence to: Dr. Aparna Wagle Shukla, Fixel Institute for Neurological disorders, 3009 Williston Road, Gainesville, FL 32608, USA; E-mail: aparna.shukla@neurology.ufl.eduSearch for more papers by this authorJill L. Ostrem MD, Jill L. Ostrem MD Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USASearch for more papers by this authorErin Patrick PhD, Erin Patrick PhD Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USASearch for more papers by this author Aparna Wagle Shukla MD, Corresponding Author Aparna Wagle Shukla MD aparna.shukla@neurology.ufl.edu Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, Florida, USA Correspondence to: Dr. Aparna Wagle Shukla, Fixel Institute for Neurological disorders, 3009 Williston Road, Gainesville, FL 32608, USA; E-mail: aparna.shukla@neurology.ufl.eduSearch for more papers by this authorJill L. Ostrem MD, Jill L. Ostrem MD Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USASearch for more papers by this authorErin Patrick PhD, Erin Patrick PhD Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USASearch for more papers by this author First published: 06 February 2021 https://doi.org/10.1002/mds.28499Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume36, Issue3March 2021Pages 610-610 RelatedInformation

Activation of peripheral nervous system (PNS) fibres to produce variable tactile and proprioceptive sensations in advanced bidirectional prosthetic limbs relies on neural stimulators with high spatial selectivity, dynamic range and resolution. A multi-channel application-specific integrated circuit (ASIC) is developed for PNS fibre activation using a wide dynamic range (10 nA-5 mA), high-resolution (30 nA step, 100 ns pulse accuracy) current stimulator, dissipating 0.73-2.75 mW at 3 V. The ASIC also enables encoding of external pressure signals via an integrate-and-fire methodology. Electrophysiological data of compound nerve action potentials were recorded for a range of stimulus amplitudes and pulse widths. This data was used to benchmark the performance of the ASIC with a known neural stimulator.

We report the development of a process/device simulation platform to model superconductor electronics. The process simulator leverages the back-end modeling capabilities of Florida object-oriented process/device/reliability simulator (FLOOXS) and builds on the existing models to simulate processes specific to Josephson junction (JJ) fabrication. The device simulator uses the process generated models and computes the normal-state electrical properties of a Nb/Al-AlO/Nb JJ. It is used to predict key operational figures of merit and how they vary with changes in process models. By integrating the process and device simulation tools, this research aims to offer an environment for the simulation of JJ circuits.