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We have used surface plasmon resonant metal gratings to induce and probe the dielectric response (i.e., electro-optic modulation) of ionic liquids (ILs) at electrode interfaces. Here, the cross-plane electric field at the electrode surface modulates the refractive index of the IL due to the Pockels effect. This is observed as a shift in the resonant angle of the grating (i.e., Δϕ), which can be related to the change in the local index of refraction of the electrolyte (i.e., Δnlocal). The reflection modulation of the IL is compared against a polar (D2O) and a non-polar solvent (benzene) to confirm the electro-optic origin of resonance shift. The electrostatic accumulation of ions from the IL induces local index changes to the gratings over the extent of electrical double layer (EDL) thickness. Finite difference time domain simulations are used to relate the observed shifts in the plasmon resonance and change in reflection to the change in the local index of refraction of the electrolyte and the thickness of the EDL. Simultaneously using the wavelength and intensity shift of the resonance enables us to determine both the effective thickness and Δn of the double layer. We believe that this technique can be used more broadly, allowing the dynamics associated with the potential-induced ordering and rearrangement of ionic species in electrode-solution interfaces.

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Memristors, non-volatile switching memory platform, has recently attracted significant interest, offering unique potential to enable the realization of human brain-like neuromorphic computing efficiency. Memristors also demonstrate excellent temperature tolerance, long-term durability, and high tunability with nanosecond pulses, making them highly attractive for neuromorphic computing applications. To better understand the material processing, microstructure, and property relationship of switching mechanisms in memristor devices, computational methodologies, and tools are developed to predict the I-V characteristics of memristor devices based on tantalum oxide (TaOx) resistive random-access memory (ReRAM) integrated with an n-channel metal-oxide-semiconductor (NMOS) transistor. A multiphysics model based on coupled partial differential equations for electrical and thermal transport phenomena is solved for the high- and low-resistance states during the formation, growth, and destruction of a conducting filament through SET and RESET stages. These stages effectively represent the migration of oxygen vacancies within an oxide exchange layer. A series of parametric studies and energy minimization calculations are conducted to determine probable ranges for key material and model parameters accounting for the experimental data. The computational model successfully predicted the measured I-V curves across various gate voltages applied to the NMOS transistor in the one transistor one resistance (1T1R) configuration.

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The oxygen diffusion rate in hafnia (HfO2)-based resistive memory plays a pivotal role in enabling nonvolatile data retention. However, the information retention times obtained in HfO2 resistive memory devices are many times higher than the expected values obtained from oxygen diffusion measurements in HfO2 materials. In this study, we resolve this discrepancy by conducting oxygen isotope tracer diffusion measurements in amorphous hafnia (a-HfO2) thin films. Our results show that the oxygen tracer diffusion in amorphous HfO2 films is orders of magnitude lower than that of previous measurements on monoclinic hafnia (m-HfO2) pellets. Moreover, oxygen tracer diffusion is much lower in denser a-HfO2 films deposited by atomic layer deposition (ALD) than in less dense a-HfO2 films deposited by sputtering. The ALD films yield similar oxygen diffusion times as experimentally measured device retention times, reconciling this discrepancy between oxygen diffusion and retention time measurements. More broadly, our work shows how processing conditions can be used to control oxygen transport characteristics in amorphous materials without long-range crystal order.

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Photonic technologies promise to deliver quantitative, multiplex, and inexpensive medical diagnostic platforms by leveraging the highly scalable processes developed for the fabrication of semiconductor microchips. However, in practice, the affordability of these platforms is limited by complex and expensive sample handling and optical alignment. We previously reported the development of a disposable photonic assay that incorporates inexpensive plastic micropillar microfluidic cards for sample delivery. That system as developed was limited to singleplex assays due to its optical configuration. To enable multiplexing, we report a new approach addressing multiplex light I/O, in which the outputs of individual grating couplers on a photonic chip are mapped to fibers in a fiber bundle. As demonstrated in the context of detecting antibody responses to influenza and SARS-CoV-2 antigens in human serum and saliva, this enables multiplexing in an inexpensive, disposable, and compact format.

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The biocompatibility of materials used in electronic devices is critical for the development of implantable devices like pacemakers and neuroprosthetics, as well as in future biomanufacturing. Biocompatibility refers to the ability of these materials to interact with living cells and tissues without causing an adverse response. Therefore, it is essential to evaluate the biocompatibility of metals and semiconductor materials used in electronic devices to ensure their safe use in medical applications. Here, we evaluated the biocompatibility of a collection of diced silicon chips coated with a variety of metal thin films, interfacing them with different cell types, including murine mastocytoma cells in suspension culture, adherent NIH 3T3 fibroblasts, and human induced pluripotent stem cell (iPSC)-derived neural progenitor cells (NPCs). All materials tested were biocompatible and showed the potential to support neural differentiation of iPSC-NPCs, creating an opportunity to use these materials in a scalable production of a range of biohybrid devices such as electronic devices to study neural behaviors and neuropathies.

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Analog hardware-based training provides a promising solution to developing state-of-the-art power-hungry artificial intelligence models. Non-volatile memory hardware such as resistive random access memory (RRAM) has the potential to provide a low power alternative. The training accuracy of analog hardware depends on RRAM switching properties including the number of discrete conductance states and conductance variability. Furthermore, the overall power consumption of the system inversely correlates with the RRAM devices conductance. To study material dependence of these properties, TaOx and HfOx RRAM devices in one-transistor one-RRAM configuration (1T1R) were fabricated using a custom 65 nm CMOS fabrication process. Analog switching performance was studied with a range of initial forming compliance current (200-500 µA) and analog switching tests with ultra-short pulse width (300 ps) was carried out. We report that by utilizing low current during electroforming and high compliance current during analog switching, a large number of RRAM conductance states can be achieved while maintaining low conductance state. While both TaOx and HfOx could be switched to more than 20 distinct states, TaOx devices exhibited 10× lower conductance, which reduces total power consumption for array-level operations. Furthermore, we adopted an analog, fully in-memory training algorithm for system-level training accuracy benchmarking and showed that implementing TaOx 1T1R cells could yield an accuracy of up to 96.4% compared to 97% for the floating-point arithmetic baseline, while implementing HfOx devices would yield a maximum accuracy of 90.5%. Our experimental work and benchmarking approach paves the path for future materials engineering in analog-AI hardware for a low-power environment training.

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There is a continuing need for biosensors that can be used in the diagnosis of COVID-19 infection and to measure a subject's immune response to the virus itself (SARS-CoV-2). In this study, grating-coupled fluorescent plasmonic (GC-FP)-based detection of SARS-CoV-2 antigens was coupled with antibody detection to yield a dual-mode detection assay. Pairs of capture and detection antibodies were screened for direct detection of the SARS-CoV-2 nucleocapsid (Nuc) antigen, which were then combined with an existing GC-FP antibody detection assay. Nuc could be detected as low as 1 µg/mL concentrations, while antibodies were detectable to 50 ng/mL. The dual detection assay was tested by adding purified Nuc antigen to serum from a polymerase chain reaction (PCR)-positive COVID-19-infected individual. Using this sample, co-detection of Nuc antigen and anti-spike protein antibodies was successfully performed on a single GC-FP chip. Total assay time was 1 h, making this the first known example of rapid dual antibody and antigen detection on the same biosensor chip.

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The diagnosis of fracture nonunion following plate osteosynthesis is subjective and frequently ambiguous. Initially following osteosynthesis, loads applied to the bone are primarily transmitted through the plate. However, as callus stiffness increases, the callus is able to bear load proportional to its stiffness while forces through the plate decrease. The purpose of this study was to use a "smart" fracture plate to distinguish between phases of fracture healing by measuring forces transmitted through the plate. A wireless force sensor and small adapter were placed on the outside of a distal femoral locking plate. The adapter converts the slight bending of the plate under axial load into a transverse force which is measurable by the sensor. An osteotomy was created and then plated in the distal femur of biomechanical Sawbones. Specimens were loaded to simulate single-leg stance first with the osteotomy defect empty (acute healing), then sequentially filled with silicone (early callus) and then polymethyl methacrylate (hard callus). There was a strong correlation between applied axial load and force measured by the "smart" plate. Data demonstrate statistically significant differences between each phase of healing with as little as 150 N of axial load applied to the femur. Forces measured in the plate were significantly different between acute (100%), early callus (66.4%), and hard callus (29.5%). This study demonstrates the potential of a "smart" fracture plate to distinguish between phases of healing. These objective data may enable early diagnosis of nonunion and enhance outcomes for patients.

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Cancer treatments utilizing biologic or cytotoxic drugs compose the frontline of therapy, and though gains in treatment efficacy have been persistent in recent decades, much work remains in understanding cancer progression and treatment. Compounding this situation is the low rate of success when translating preclinical drug candidates to the clinic, which raises costs and development timelines. This underperformance is due in part to the poor recapitulation of the tumor microenvironment, a critical component of cancer biology, in cancer model systems. New technologies capable of both accurately observing and manipulating the tumor microenvironment are needed to effectively model cancer response to treatment. In this review, conventional cancer models are summarized, and a primer on emerging techniques for monitoring and modulating the tumor microenvironment is presented and discussed.

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Measuring the antibody response to 2019 SARS CoV2 is critical for diagnostic purposes, for monitoring the prevalence of infection, and for gauging the efficacy of the worldwide vaccination effort for COVID-19. In this study, a microchip-based grating-coupled fluorescent plasmonic (GC-FP) assay was used to measure antibody levels that resulted from COVID-19 infection and vaccination. In addition, we measured the relative antibody binding toward antigens from the CoV2 virus variants strains B.1.1.7 (Alpha) and B.1.351 (Beta). Antibody levels against multiple antigens within the SARS CoV2 spike protein were significantly elevated for both vaccinated and infected individuals, while those against the nucleocapsid (N) protein were only elevated for infected individuals. GC-FP was effective for monitoring the IgG-based serological response to vaccination throughout the vaccination sequence and also resolved acute (within hours) increases in antibody levels. A significant decrease in antibody binding to antigens from the B.1.351 variant, but not B.1.1.7, was observed for all vaccinated subjects when measured by GC-FP compared to the 2019 SARS CoV2 antigens. These results were corroborated by competitive enzyme-linked immunosorbent assay (ELISA). Collectively, the findings suggest that GC-FP is a viable, rapid, and accurate method for measuring both overall antibody levels to SARS CoV2 and relative antibody binding to viral variants during infection or vaccination. IMPORTANCE In this work, a novel biosensor technology was used to measure antibody levels that resulted from vaccination against COVID-19 and/or from infection with the virus. Importantly, this approach enables quantification of antibody levels, which can provide information about the timing and level of immune response. Due the multiplexed nature of this approach, antibody binding to both the original 2019 SARS CoV-2 strain and variant strains can be performed simultaneously and in a short (30-min) time frame.

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Cell viability is an essential facet of mammalian and microbial bioprocessing. While robust methods of monitoring cellular health remain critically important to biomanufacturing and biofabrication, the complexity of advanced cell culture platforms often poses challenges for conventional viability assays. This review surveys novel approaches to discern the metabolic, morphological, and mechanistic hallmarks of living systems - spanning subcellular and multicellular scales. While fluorescent probes coupled with 3D image analysis generate rapid results with spatiotemporal detail, molecular techniques like viability PCR can distinguish live cells with genetic specificity. Notably, label-free biosensors can detect nuanced attributes of cellular vital signs with single-cell resolution via optical, acoustic, and electrical signals. Ultimately, efforts to integrate these modalities with automation, machine learning, and high-throughput workflows will lead to exciting new vistas across the cell viability landscape.

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Decades of research have shown that biosensors using photonic circuits fabricated using CMOS processes can be highly sensitive, selective, and quantitative. Unfortunately, the cost of these sensors combined with the complexity of sample handling systems has limited the use of such sensors in clinical diagnostics. We present a new "disposable photonics" sensor platform in which rice-sized (1 × 4 mm) silicon nitride ring resonator sensor chips are paired with plastic micropillar fluidic cards for sample handling and optical detection. We demonstrate the utility of the platform in the context of detecting human antibodies to SARS-CoV-2, both in convalescent COVID-19 patients and for subjects undergoing vaccination. Given its ability to provide quantitative data on human samples in a simple, low-cost single-use format, we anticipate that this platform will find broad utility in clinical diagnostics for a broad range of assays.

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Lyme disease, which is primarily caused by infection with the bacterium Borrelia burgdorferi in the United States or other Borrelia species internationally, presents an ongoing challenge for diagnostics. Serological testing is the primary means of diagnosis but testing approaches differ widely, with varying degrees of sensitivity and specificity. Moreover, there is currently no reliable test to determine disease resolution following treatment. A distinct challenge in Lyme disease diagnostics is the variable patterns of human immune response to a plurality of antigens presented by Borrelia spp. during the infection. Thus, multiplexed testing approaches that capture these patterns and detect serological response against multiple antigens may be the key to prompt, accurate Lyme disease diagnosis. In this review, current state-of-the-art multiplexed diagnostic approaches are presented and compared with respect to their diagnostic accuracy and their potential for monitoring response to treatment.

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Precisely controlling delivery of drugs and other reagents is important for intravital microscopy studies. In this work, photolithographic integration of micro-nozzles onto a microfluidic platform was performed to tune the fluid flow profile and depth of penetration into biological tissue mimics. Performance characteristics were measured by correlating the flow rate through the device to the applied pressure and/or delivery of dyes into solution and agarose gel-based phantom tissue. From these results, the implementation of micro-nozzles was demonstrated to significantly improve the lateral dispersion of delivered fluid and increase the depth of penetration into phantom tissue.

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The 2019 SARS CoV-2 (COVID-19) pandemic has illustrated the need for rapid and accurate diagnostic tests. In this work, a multiplexed grating-coupled fluorescent plasmonics (GC-FP) biosensor platform was used to rapidly and accurately measure antibodies against COVID-19 in human blood serum and dried blood spot samples. The GC-FP platform measures antibody-antigen binding interactions for multiple targets in a single sample, and has 100% selectivity and sensitivity (n = 23) when measuring serum IgG levels against three COVID-19 antigens (spike S1, spike S1S2, and the nucleocapsid protein). The GC-FP platform yielded a quantitative, linear response for serum samples diluted to as low as 1:1600 dilution. Test results were highly correlated with two commercial COVID-19 antibody tests, including an enzyme linked immunosorbent assay (ELISA) and a Luminex-based microsphere immunoassay. To demonstrate test efficacy with other sample matrices, dried blood spot samples (n = 63) were obtained and evaluated with GC-FP, yielding 100% selectivity and 86.7% sensitivity for diagnosing prior COVID-19 infection. The test was also evaluated for detection of multiple immunoglobulin isotypes, with successful detection of IgM, IgG and IgA antibody-antigen interactions. Last, a machine learning approach was developed to accurately score patient samples for prior COVID-19 infection, using antibody binding data for all three COVID-19 antigens used in the test.

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The results from this study demonstrate the potential of an AlGaN/GaN high electron mobility transistor sensor for the detection of reactive and transient biological molecules such as hydrogen peroxide. A boronate-based fluorescent probe was used with this device to detect the presence of micromolar levels of hydrogen peroxide typically associated with intracellular processes. The real-time electrical response of the high electron mobility transistor sensor showed a gradual decrease in the two-dimensional electron gas current as the reaction proceeded over time. A corresponding increase in the emission intensity was measured from the fluorescent probe with the progression of the reaction. The fluorescence from the boronate probe was used as an indicator to confirm the detection of hydrogen peroxide. These results demonstrate the dynamic measurement capability of AlGaN/GaN high electron mobility transistor sensors in monitoring real-time reactions of reactive oxygen species such as hydrogen peroxide.

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Silver nanoparticles (AgNPs) are used in food packaging materials, dental care products and other consumer goods and can result in oral exposure. To determine whether AgNP coatings modulate transcriptional responses to AgNP exposure, we exposed mice orally to 20 nm citrate (cit)-coated AgNPs (cit-AgNPs) or polyvinylpyrrolidone (PVP)-coated AgNPs (PVP-AgNPs) at a 4 mg/kg dose for 7 consecutive days and analyzed changes in the expression of protein-coding genes and long noncoding RNAs (lncRNAs), a new class of regulatory RNAs, in the liver. We identified unique and common expression signatures of protein-coding and lncRNA genes, altered biological processes and signaling pathways, and coding-non-coding gene interactions for cit-AgNPs and PVP-AgNPs. Commonly regulated genes comprised only about 10 and 20 percent of all differentially expressed genes in PVP-AgNP and cit-AgNP exposed mice, respectively. Commonly regulated biological processes included glutathione metabolic process and cellular oxidant detoxification. Commonly regulated pathways included Keap-Nrf2, PPAR, MAPK and IL-6 signaling pathways. The coding-non-coding gene co-expression analysis revealed that protein-coding genes were co-expressed with a variable number of lncRNAs ranging from one to twenty three and may share functional roles with the protein-coding genes. PVP-AgNP exposure induced a more robust transcriptional response than cit-AgNP exposure characterized by more than two-fold higher number of differentially expressed both protein- coding and lncRNA genes. Our data demonstrate that the surface coating strongly modulates the spectrum and the number of differentially expressed genes after oral AgNP exposure. On the other hand, our data suggest that AgNP exposure can alter drug and chemical sensitivity, metabolic homeostasis and cancer risk irrespective of the coating type, warranting further investigations.

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We demonstrate the hot electron injection of photoexcited carriers in an Ag-based plasmon resonant grating structure. By varying the incident angle of irradiation, sharp dips are observed in the reflectance with p-polarized light (electric field perpendicular to grating lines) when there is wavevector matching between the incident light and the plasmon resonant modes of the grating and no angle dependence is observed with s-polarized light. This configuration enables us to compare photoelectrochemical current produced by plasmon resonant excitation with that of bulk metal interband absorption simply by rotating the polarization of the incident light while keeping all other parameters of the measurement fixed. With 633 nm light, we observed a 12-fold enhancement in the photocurrent (i.e., reaction rate) between resonant and nonresonant polarizations at incident angles of ±7.6° from normal. At 785 nm irradiation, we observed similar resonant profiles to those obtained with 633 nm wavelength light but with a 44-fold enhancement factor. Using 532 nm light, we observed two resonant peaks (with approximately 10× enhancement) in the photocurrent at 19.4° and 28.0° incident angles, each corresponding to higher order modes in the grating with more nodes per period. The lower enhancement factors observed at shorter wavelengths are attributed to interband transitions, which provide a damping mechanism for the plasmon resonance. Finite difference time domain (FDTD) simulations of these grating structures confirm the resonant profiles observed in the angle-dependent spectra of these gratings and provide a detailed picture of the electric field profiles on and off resonance.

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Lyme disease (LD) diagnosis using the current two-tier algorithm is constrained by low sensitivity for early-stage infection and ambiguity in determining treatment response. We recently developed a protein microarray biochip that measures diagnostic serum antibody targets using grating-coupled fluorescent plasmonics (GC-FP) technology. This strategy requires microliters of blood serum to enable multiplexed biomarker screening on a compact surface and generates quantitative results that can be further processed for diagnostic scoring. The GC-FP biochip was used to detect serum antibodies in patients with active and convalescent LD, as well as various negative controls. We hypothesized that the quantitative, high-sensitivity attributes of the GC-FP approach permit: 1) screening of antibody targets predictive for LD status, and 2) development a diagnostic algorithm that is more sensitive, specific, and informative than the standard ELISA and Western blot assays. Notably, our findings led to a diagnostic algorithm that may be more sensitive than the current standard for detecting early LD, while maintaining 100% specificity. We further show that analysis of relative antibody levels to predict disease status, such as in acute and convalescent stages of infection, is possible with a highly sensitive and quantitative platform like GC-FP. The results from this study add to the urgent conversation regarding better diagnostic strategies and more effective treatment for patients affected by tick-borne disease.

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Advances in integrated circuitry from the 1950s to the present day have enabled a revolution in technology across the world. However, fundamental limits of circuitry make further improvements through historically successful methods increasingly challenging. It is becoming clear that to address new challenges and applications, new methods of computation will be required. One promising field is neuromorphic engineering, a broad field which applies biologically inspired principles to create alternative computational architectures and methods. We address why neuromorphic engineering is one of the most promising fields within emerging computational technology, elaborating on its common principles and models, and discussing its current state and future challenges.