Plasmonic Optical Fiber Based Continuous in-Vivo Glucose Monitoring for ICU/CCU Setup.

This paper reports a sensor architecture for continuous monitoring of biomarkers directly in the blood, especially for ICU/CCU patients requiring critical care and rapid biomarker measurement. The sensor is based on a simple optical fiber that can be inserted through a catheter into the bloodstream, wherein gold nanoparticles are attached at its far distal end as a plasmonic material for highly sensitive opto-chemical sensing of target biomolecules (glucose in our application) via the excitation of surface plasmon polaritons. For specificity, the nanoparticles are functionalized with a specific receptor enzyme that enables the localized surface plasmon resonance (LSPR)-based targeted bio-sensing. Further, a micro dialysis probe is introduced in the proposed architecture, which facilitates continuous monitoring for an extended period without fouling the sensor surface with cells and blood debris present in whole blood, leading to prolonged enhanced sensitivity and limit of detection, relative to existing state-of-the-art continuous monitoring devices that can conduct direct measurements in blood. To establish this proof-of-concept, we tested the sensor device to monitor glucose in-vivo involving an animal model, where continuous monitoring was done directly in the circulation of living rats. The sensor's sensitivity to glucose was found to be 0.0354 a.u./mg.dl-1 with a detection limit of 50.89 mg/dl.

Development of a small cell lung cancer organoid model to study cellular interactions and survival after chemotherapy.

Introduction: Small-cell-lung-cancer (SCLC) has the worst prognosis of all lung cancers because of a high incidence of relapse after therapy. While lung cancer is the second most common malignancy in the US, only about 10% of cases of lung cancer are SCLC, therefore, it is categorized as a rare and recalcitrant disease. Therapeutic discovery for SCLC has been challenging and the existing pre-clinical models often fail to recapitulate actual tumor pathophysiology. To address this, we developed a bioengineered 3-dimensional (3D) SCLC co-culture organoid model as a phenotypic tool to study SCLC tumor kinetics and SCLC-fibroblast interactions after chemotherapy. Method: We used functionalized alginate microbeads as a scaffold to mimic lung alveolar architecture and co-cultured SCLC cell lines with primary adult lung fibroblasts (ALF). We found that SCLCs in the model proliferated extensively, invaded the microbead scaffold and formed tumors within just 7 days. We compared the bioengineered tumors with patient tumors and found them to recapitulate the pathology and immunophenotyping of the patient tumors. When treated with standard chemotherapy drugs, etoposide and cisplatin, we observed that some of the cells survived the chemotherapy and reformed the tumor in the organoid model. Result and Discussion: Co-culture of the SCLC cells with ALFs revealed that the fibroblasts play a key role in inducing faster and more robust SCLC cell regrowth in the model. This is likely due to a paracrine effect, as conditioned media from the same fibroblasts could also support this accelerated regrowth. This model can be used to study cell-cell interactions and the response to chemotherapy in SCLC and is also scalable and amenable to high throughput phenotypic or targeted drug screening to find new therapeutics for SCLC.

Small cell lung cancer co-culture organoids provide insights into cancer cell survival after chemotherapy.

Small-cell-lung-cancer (SCLC) has the worst prognosis of all lung cancers because of a high incidence of relapse after therapy. We developed a bioengineered 3-dimensional (3D) SCLC co-culture organoid as a phenotypic tool to study SCLC tumor kinetics and SCLC-fibroblast interactions during relapse. We used functionalized alginate microbeads as a scaffold to mimic lung alveolar architecture and co-cultured SCLC cell lines with primary adult lung fibroblasts (ALF). We found that SCLCs in the model proliferated extensively, invaded the microbead scaffold and formed tumors within just 7 days. We compared the bioengineered tumors with patient tumors and found them to recapitulate the pathology and immunophenotyping of the patient tumors better than the PDX model developed from the same SCLC cell line. When treated with standard chemotherapy drugs, etoposide and cisplatin, the organoid recapitulated relapse after chemotherapy. Co-culture of the SCLC cells with ALFs revealed that the fibroblasts play a key role in inducing faster and more robust SCLC cell regrowth in the model. This was a paracrine effect as conditioned medium from the same fibroblasts was responsible for this accelerated cell regrowth. This model is also amenable to high throughput phenotypic or targeted drug screening to find new therapeutics for SCLC.

A processing-in-pixel-in-memory paradigm for resource-constrained TinyML applications.

The demand to process vast amounts of data generated from state-of-the-art high resolution cameras has motivated novel energy-efficient on-device AI solutions. Visual data in such cameras are usually captured in analog voltages by a sensor pixel array, and then converted to the digital domain for subsequent AI processing using analog-to-digital converters (ADC). Recent research has tried to take advantage of massively parallel low-power analog/digital computing in the form of near- and in-sensor processing, in which the AI computation is performed partly in the periphery of the pixel array and partly in a separate on-board CPU/accelerator. Unfortunately, high-resolution input images still need to be streamed between the camera and the AI processing unit, frame by frame, causing energy, bandwidth, and security bottlenecks. To mitigate this problem, we propose a novel Processing-in-Pixel-in-memory (P2M) paradigm, that customizes the pixel array by adding support for analog multi-channel, multi-bit convolution, batch normalization, and Rectified Linear Units (ReLU). Our solution includes a holistic algorithm-circuit co-design approach and the resulting P2M paradigm can be used as a drop-in replacement for embedding memory-intensive first few layers of convolutional neural network (CNN) models within foundry-manufacturable CMOS image sensor platforms. Our experimental results indicate that P2M reduces data transfer bandwidth from sensors and analog to digital conversions by [Formula: see text], and the energy-delay product (EDP) incurred in processing a MobileNetV2 model on a TinyML use case for visual wake words dataset (VWW) by up to [Formula: see text] compared to standard near-processing or in-sensor implementations, without any significant drop in test accuracy.

ACE-SNN: Algorithm-Hardware Co-design of Energy-Efficient & Low-Latency Deep Spiking Neural Networks for 3D Image Recognition.

High-quality 3D image recognition is an important component of many vision and robotics systems. However, the accurate processing of these images requires the use of compute-expensive 3D Convolutional Neural Networks (CNNs). To address this challenge, we propose the use of Spiking Neural Networks (SNNs) that are generated from iso-architecture CNNs and trained with quantization-aware gradient descent to optimize their weights, membrane leak, and firing thresholds. During both training and inference, the analog pixel values of a 3D image are directly applied to the input layer of the SNN without the need to convert to a spike-train. This significantly reduces the training and inference latency and results in high degree of activation sparsity, which yields significant improvements in computational efficiency. However, this introduces energy-hungry digital multiplications in the first layer of our models, which we propose to mitigate using a processing-in-memory (PIM) architecture. To evaluate our proposal, we propose a 3D and a 3D/2D hybrid SNN-compatible convolutional architecture and choose hyperspectral imaging (HSI) as an application for 3D image recognition. We achieve overall test accuracy of 98.68, 99.50, and 97.95% with 5 time steps (inference latency) and 6-bit weight quantization on the Indian Pines, Pavia University, and Salinas Scene datasets, respectively. In particular, our models implemented using standard digital hardware achieved accuracies similar to state-of-the-art (SOTA) with ~560.6× and ~44.8× less average energy than an iso-architecture full-precision and 6-bit quantized CNN, respectively. Adopting the PIM architecture in the first layer, further improves the average energy, delay, and energy-delay-product (EDP) by 30, 7, and 38%, respectively.

Multiple nano-filaments based efficient resistive switching in TiO2 nanotubes array influenced by thermally induced self-doping and anatase to rutile phase transformation.

In this paper, the impact of thermally induced self-doping and phase transformation in TiO2 based resistive random-access memory (ReRAM) is discussed. Instead of a thin film, a vertically aligned one-dimensional TiO2 nanotube array (TNTA) was used as a switching element. Anodic oxidation method was employed to synthesize TNTA, which was thermally treated in the air at 350 °C followed by further annealing from 350 °C to 650 °C in argon. Au/TiO2 nanotube/Ti resistive switching devices were fabricated with porous gold (Au) top electrode. The x-ray diffraction results along with Raman spectra evidently demonstrate a change in phase of crystallinity from anatase to rutile, whereas photoluminescence spectra revealed the self-doping level in terms of oxygen vacancies (OV) and Ti interstitials (Tii) as the temperature of thermal treatment gets increased. The electrical characterizations establish the bipolar and electroforming free resistive switching in all the samples. Among those, the ReRAM sample S3 thermally treated at 550 °C displayed the most effective resistive switching properties with R OFF/R ON of 102 at a read voltage of -0.6 V and a SET voltage of -2.0 V. Moreover, the S3 sample showed excellent retention performance for over 106 s, where stable R OFF/R ON ≈ 107 was maintained throughout the experiment.

Plasmonic Ag nanoparticles arbitrated enhanced photodetection in p-NiO/n-rGO heterojunction for future self-powered UV photodetectors.

We report on the low cost and low temperature chemical synthesis of p-type nickel oxide (NiO) and n-type reduced graphene oxide (rGO) and their integration onto ITO/glass substrate to form p-NiO/n-rGO heterojunction for possible self-powered ultraviolet (UV) photodetector applications. Different spectroscopies and microscopes were employed to study their microstructural and surface properties. Whereas, the electrical characterizations have been performed on the devices to ascertain the responsivity, detectivity, external quantum efficiency and temporal responses under dark and UV illumination. It is noteworthy that rGO has not only been used as an n-type semiconductor, but also acted as an electron transport layer, which satisfactorily separates out the electrons from the generated carrier pairs, leading to enhanced photoresponse. Furthermore, efforts were also consecrated to synthesize Ag nanoparticles (NPs) of ∼5 nm radius. The integration of Ag NPs on the conventional NiO/rGO heterojunction facilitates an improved UV light absorption property. It was understood that the performance improvement was owed to the local surface plasmon resonance of Ag NPs within the active layer of NiO. Surprisingly, both the devices (with and without Ag NPs) exhibit photovoltaic behavior which shows its potential for self-powered device application. When the Ag NPs embedded device is concerned, it showed better on/off ratio (6.3 × 103), high responsivity (72 mAW-1), large detectivity (3.95 × 1012 Jones), and high efficiency (24.46%) as compared to the conventional NiO/rGO heterojunction one (without Ag NPs). The variation in the photoresponse and improved charge transport was explained through a band-diagram, which also showcases a comprehensive understanding on the operational principle of the fabricated self-powered devices. Thus, this self-powered photodetector driven by built in electric field is operated independently and can be attached with any other electronic gadgets for internet of things applications.

Understanding Keesom Interactions in Monolayer-Based Large-Area Tunneling Junctions.

Charge transport across self-assembled monolayers (SAMs) has been widely studied. Discrepancies of charge tunneling data that arise from various studies, however, call for efforts to develop new statistical analytical approaches to understand charge tunneling across SAMs. Structure-property studies on charge tunneling across SAM-based junctions have largely been through comparison of average tunneling rates and associated variance. These early moments (especially the average) are dominated by barrier width-a static property of the junction. In this work, we show that analysis of higher statistical moments (skewness and kurtosis) reveals the dynamic nature of the tunnel junction. Intramolecular Keesom (dipole-dipole) interactions dynamically fluctuate with bias as dictated by stereoelectronic limitations. Analyzing variance in the distribution of tunneling data instead of the first statistical moment (average), for a series of n-alkanethiols containing internal amide and aromatic terminal groups, we observe that the direction of dipole moments affects molecule-electrode coupling. An applied bias induces changes in the tunneling probability, affecting the distribution of tunneling paths in large-area molecular junctions.

Lead-free epitaxial ferroelectric material integration on semiconducting (100) Nb-doped SrTiO3 for low-power non-volatile memory and efficient ultraviolet ray detection.

We report lead-free ferroelectric based resistive switching non-volatile memory (NVM) devices with epitaxial (1-x)BaTiO3-xBiFeO3 (x = 0.725) (BT-BFO) film integrated on semiconducting (100) Nb (0.7%) doped SrTiO3 (Nb:STO) substrates. The piezoelectric force microscopy (PFM) measurement at room temperature demonstrated ferroelectricity in the BT-BFO thin film. PFM results also reveal the repeatable polarization inversion by poling, manifesting its potential for read-write operation in NVM devices. The electroforming-free and ferroelectric polarization coupled electrical behaviour demonstrated excellent resistive switching with high retention time, cyclic endurance, and low set/reset voltages. X-ray photoelectron spectroscopy was utilized to determine the band alignment at the BT-BFO and Nb:STO heterojunction, and it exhibited staggered band alignment. This heterojunction is found to behave as an efficient ultraviolet photo-detector with low rise and fall time. The architecture also demonstrates half-wave rectification under low and high input signal frequencies, where the output distortion is minimal. The results provide avenue for an electrical switch that can regulate the pixels in low or high frequency images. Combined this work paves the pathway towards designing future generation low-power ferroelectric based microelectronic devices by merging both electrical and photovoltaic properties of BT-BFO materials.

Integration of lead-free ferroelectric on HfO2/Si (100) for high performance non-volatile memory applications.

We introduce a novel lead-free ferroelectric thin film (1-x)BaTiO3-xBa(Cu1/3Nb2/3)O3 (x = 0.025) (BT-BCN) integrated on to HfO2 buffered Si for non-volatile memory (NVM) applications. Piezoelectric force microscopy (PFM), x-ray diffraction, and high resolution transmission electron microscopy were employed to establish the ferroelectricity in BT-BCN thin films. PFM study reveals that the domains reversal occurs with 180° phase change by applying external voltage, demonstrating its effectiveness for NVM device applications. X-ray photoelectron microscopy was used to investigate the band alignments between atomic layer deposited HfO2 and pulsed laser deposited BT-BCN films. Programming and erasing operations were explained on the basis of band-alignments. The structure offers large memory window, low leakage current, and high and low capacitance values that were easily distinguishable even after ~10(6) s, indicating strong charge storage potential. This study explains a new approach towards the realization of ferroelectric based memory devices integrated on Si platform and also opens up a new possibility to embed the system within current complementary metal-oxide-semiconductor processing technology.

Integration of SrTiO3 on crystallographically oriented epitaxial germanium for low-power device applications.

SrTiO3 integration on crystallographic oriented (100), (110), and (111) epitaxial germanium (Ge) exhibits a potential for a new class of nanoscale transistors. Germanium is attractive due to its superior transport properties while SrTiO3 (STO) is promising due to its high relative permittivity, both being critical parameters for next-generation low-voltage and low-leakage metal-oxide semiconductor field-effect transistors. The sharp heterointerface between STO and each crystallographically oriented Ge layer, studied by cross-sectional transmission electron microscopy, as well as band offset parameters at each heterojunction offers a significant advancement for designing a new generation of ferroelectric-germanium based multifunctional devices. Moreover, STO, when used as an interlayer between metal and n-type (4 × 10(18) cm(-3)) epitaxial Ge in metal-insulator-semiconductor (MIS) structures, showed a 1000 times increase in current density as well as a decrease in specific contact resistance. Furthermore, the inclusion of STO on n-Ge demonstrated the first experimental findings of the MIS behavior of STO on n-Ge.

An improved parent-centric mutation with normalized neighborhoods for inducing niching behavior in differential evolution.

In real life, we often need to find multiple optimally sustainable solutions of an optimization problem. Evolutionary multimodal optimization algorithms can be very helpful in such cases. They detect and maintain multiple optimal solutions during the run by incorporating specialized niching operations in their actual framework. Differential evolution (DE) is a powerful evolutionary algorithm (EA) well-known for its ability and efficiency as a single peak global optimizer for continuous spaces. This article suggests a niching scheme integrated with DE for achieving a stable and efficient niching behavior by combining the newly proposed parent-centric mutation operator with synchronous crowding replacement rule. The proposed approach is designed by considering the difficulties associated with the problem dependent niching parameters (like niche radius) and does not make use of such control parameter. The mutation operator helps to maintain the population diversity at an optimum level by using well-defined local neighborhoods. Based on a comparative study involving 13 well-known state-of-the-art niching EAs tested on an extensive collection of benchmarks, we observe a consistent statistical superiority enjoyed by our proposed niching algorithm.

A spatially informative optic flow model of bee colony with saccadic flight strategy for global optimization.

This paper presents a novel search metaheuristic inspired from the physical interpretation of the optic flow of information in honeybees about the spatial surroundings that help them orient themselves and navigate through search space while foraging. The interpreted behavior combined with the minimal foraging is simulated by the artificial bee colony algorithm to develop a robust search technique that exhibits elevated performance in multidimensional objective space. Through detailed experimental study and rigorous analysis, we highlight the statistical superiority enjoyed by our algorithm over a wide variety of functions as compared to some highly competitive state-of-the-art methods.