Investigation of the surface mechanical properties of functionalized single-walled carbon nanotube (SWCNT) reinforced PDMS nanocomposites using nanoindentation analysis.

Functionalizing single-walled carbon nanotubes (SWCNT) with different chemical functional groups directly enhances their chemical adhesion and dispersion in viscous polymeric resins such as polydimethylsiloxane (PDMS). Nevertheless, the ideal surface polarity (hydrophilic or hydrophobic) for SWCNT to foster stronger chemical bonding with PDMS remains uncertain. This investigation delves into the impact of enhanced SWCNT dispersion within PDMS on the surface mechanical characteristics of this flexible composite system. We use carboxylic acid-functionalized SWCNT (COOH-SWCNT) and silane-functionalized SWCNT (sily-SWCNT), recognized for their hydrophilic and hydrophobic surface polarities, respectively, as reinforcing agents at ultra-low weight percentage loadings: 0.05 wt%, 0.5 wt%, and 1 wt%. We perform quasi-static nanoindentation analysis employing a Berkovich tip to probe the localized mechanical behavior of PDMS-SWCNT films at an indentation depth of 1 µm. Plastic deformation within the samples, denoted as plastic work (Wp), as well as the elastic modulus (E), hardness (H), and contact stiffness (Sc) of the composites are examined from the force-displacement curves to elucidate the enhancement in the surface mechanical attributes of the composite films.

Hardware implementation of memristor-based artificial neural networks.

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.

Enhancement of the mechanical properties in ultra-low weight SWCNT sandwiched PDMS composites using a novel stacked architecture.

This study focuses on enhancing the mechanical properties of thin, soft, free-standing films via a layer-by-layer (LBL) fabrication process called LBL-FP. Soft polymer nanocomposite (PNC) thin films, combining polydimethylsiloxane (PDMS) and single-walled carbon nanotubes (SWCNT) at ultra-low loadings using a unique bottom-up LBL-FP, are examined. Two different structures of layered composites, (i) LBL PNCs- Layered composites with alternating layers of PDMS and SWCNT, (ii) Bulk PNCs- Layered composites with SWCNT dispersed in the bulk of PDMS, are comparatively investigated for their structural and mechanical properties. Silane-functionalized SWCNT strengthens the chemical bonding with PDMS, improving adhesion and dispersion. Mechanical analysis using nanoindentation, delamination, and dynamic analysis highlights the advantages of LBL PNCs with alternating layers of PDMS and SWCNT. Notably, LBL PNC (0.5 wt%) exhibits significant improvements, such as 2.6X increased nanoindentation resistance, 3X improved viscoelasticity, and (2-5)X enhanced tensile properties in comparison with neat PDMS. Due to this, LBL PNCs offer potential for soft, lightweight applications like wearables, electromagnetic interference shielding materials, and strain sensors while advancing composite thin film mechanics. The study emphasizes using a stacked architecture to produce PDMS-SWCNT multilayered PNCs with improved mechanics utilizing ultra-low concentrations of SWCNT. This first-of-its-kind stack design facilitates possibilities for lightweight composites utilizing less fillers. The LBL assembly involves the stacking of alternating layers of different materials, each contributing specific properties to enhance the overall strength and toughness of the structure.

Dielectric breakdown of 2D muscovite mica.

Localized electrical breakdown (BD) measurements are performed on 2D muscovite mica flakes of ~ 2 to 15 nm thickness using Conduction Atomic Force Microscopy (CAFM). To obtain robust BD data by CAFM, the probed locations are spaced sufficiently far apart (> 1 µm) to avoid mutual interference and the maximum current is set to a low value (< 1 nA) to ensure severe damage does not occur to the sample. The analyses reveals that 2D muscovite mica has high electrical breakdown strength (12 MV/cm or more) and low leakage current, comparable to 2D hexagonal boron nitride (h-BN) of similar thickness. However, a significant difference compared to h-BN is the very low current necessary to avoid catastrophic damage during the BD event, even for very thin (2-3 nm) flakes. Further, for mica the BD transient always appear to be very abrupt, and no progressive BD process was definitively observed. These marked differences between mica and h-BN are attributed to the poor thermal conductivity of mica.

Remaining Useful Life Prediction of Lithium-Ion Batteries Using Neural Networks with Adaptive Bayesian Learning.

With smart electronic devices delving deeper into our everyday lives, predictive maintenance solutions are gaining more traction in the electronic manufacturing industry. It is imperative for the manufacturers to identify potential failures and predict the system/device's remaining useful life (RUL). Although data-driven models are commonly used for prognostic applications, they are limited by the necessity of large training datasets and also the optimization algorithms used in such methods run into local minima problems. In order to overcome these drawbacks, we train a Neural Network with Bayesian inference. In this work, we use Neural Networks (NN) as the prediction model and an adaptive Bayesian learning approach to estimate the RUL of electronic devices. The proposed prognostic approach functions in two stages-weight regularization using adaptive Bayesian learning and prognosis using NN. A Bayesian framework (particle filter algorithm) is adopted in the first stage to estimate the network parameters (weights and bias) using the NN prediction model as the state transition function. However, using a higher number of hidden neurons in the NN prediction model leads to particle weight decay in the Bayesian framework. To overcome the weight decay issues, we propose particle roughening as a weight regularization method in the Bayesian framework wherein a small Gaussian jitter is added to the decaying particles. Additionally, weight regularization was also performed by adopting conventional resampling strategies to evaluate the efficiency and robustness of the proposed approach and to reduce optimization problems commonly encountered in NN models. In the second stage, the estimated distributions of network parameters were fed into the NN prediction model to predict the RUL of the device. The lithium-ion battery capacity degradation data (CALCE/NASA) were used to test the proposed method, and RMSE values and execution time were used as metrics to evaluate the performance.

Modeling Impact Mechanics of 3D Helicoidally Architected Polymer Composites Enabled by Additive Manufacturing for Lightweight Silicon Photovoltaics Technology.

When silicon solar cells are used in the novel lightweight photovoltaic (PV) modules using a sandwich design with polycarbonate sheets on both the front and back sides of the cells, they are much more prone to impact loading, which may be prevalent in four-season countries during wintertime. Yet, the lightweight PV modules have recently become an increasingly important development, especially for certain segments of the renewable energy markets all over the world-such as exhibition halls, factories, supermarkets, farms, etc.-including in countries with harsh hailstorms during winter. Even in the standard PV module design using glass as the front sheet, the silicon cells inside remain fragile and may be prone to impact loading. This impact loading has been widely known to lead to cracks in the silicon solar cells that over an extended period of time may significantly degrade performance (output power). In our group's previous work, a 3D helicoidally architected fiber-based polymer composite (enabled by an electrospinning-based additive manufacturing methodology) was found to exhibit excellent impact resistance-absorbing much of the energy from the impact load-such that the silicon solar cells encapsulated on both sides by this material breaks only at significantly higher impact load/energy, compared to when a standard, commercial PV encapsulant material was used. In the present study, we aim to use numerical simulation and modeling to enhance our understanding of the stress distribution and evolution during impact loading on such helicoidally arranged fiber-based composite materials, and thus the damage evolution and mechanisms. This could further aid the implementation of the lightweight PV technology for the unique market needs, especially in countries with extreme winter seasons.

Crystallographic Anisotropy Dependence of Interfacial Sliding Phenomenon in a Cu(16)/Nb(16) ARB (Accumulated Rolling Bonding) Nanolaminate.

Nanolaminates are extensively studied due to their unique properties, such as impact resistance, high fracture toughness, high strength, and resistance to radiation damage. Varieties of nanolaminates are being fabricated to achieve high strength and fracture toughness. In this study, one such nanolaminate fabricated through accumulative roll bonding (Cu(16)/Nb(16) ARB nanolaminate, where 16 nm is the layer thickness) was used as a test material. Cu(16)/Nb(16) ARB nanolaminate exhibits crystallographic anisotropy due to the existence of distinct interfaces along the rolling direction (RD) and the transverse direction (TD). Nanoindentation was executed using a Berkovich tip, with the main axis oriented either along TD or RD of the Cu(16)/Nb(16) ARB nanolaminate. Subsequently, height profiles were obtained along the main axis of the Berkovich indent for both TD and RD using scanning probe microscopy (SPM), which was later used to estimate the pile-up along the RD and TD. The RD exhibited more pile-up than the TD due to the anisotropy of the Cu(16)/Nb(16) ARB interface and the material plasticity along the TD and RD. An axisymmetric 2D finite element analysis (FEA) was also performed to compare/validate nanoindentation data, such as load vs. displacement curves and pile-up. The FEA simulated load vs. displacement curves matched relatively well with the experimentally generated load-displacement curves, while qualitative agreement was found between the simulated pile-up data and the experimentally obtained pile-up data. The authors believe that pile-up characterization during indentation is of great importance to documenting anisotropy in nanolaminates.

Standards for the Characterization of Endurance in Resistive Switching Devices.

Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to variability and reliability issues, which are usually evaluated through switching endurance tests. However, we note that most studies that claimed high endurances >106 cycles were based on resistance versus cycle plots that contain very few data points (in many cases even <20), and which are collected in only one device. We recommend not to use such a characterization method because it is highly inaccurate and unreliable (i.e., it cannot reliably demonstrate that the device effectively switches in every cycle and it ignores cycle-to-cycle and device-to-device variability). This has created a blurry vision of the real performance of RS devices and in many cases has exaggerated their potential. This article proposes and describes a method for the correct characterization of switching endurance in RS devices; this method aims to construct endurance plots showing one data point per cycle and resistive state and combine data from multiple devices. Adopting this recommended method should result in more reliable literature in the field of RS technologies, which should accelerate their integration in commercial products.

Impact-Resistant and Tough 3D Helicoidally Architected Polymer Composites Enabling Next-Generation Lightweight Silicon Photovoltaics Module Design and Technology.

Lightweight photovoltaics (PV) modules are important for certain segments of the renewable energy markets-such as exhibition halls, factories, supermarkets, farms, etc. However, lightweight silicon-based PV modules have their own set of technical challenges or concerns. One of them, which is the subject of this paper, is the lack of impact resistance, especially against hailstorms in deep winter in countries with four seasons. Even if the front sheet can be made sufficiently strong and impact-resistant, the silicon cells inside remain fragile and very prone to impact loading. This leads to cracks that significantly degrade performance (output power) over time. A 3D helicoidally architected fiber-based polymer composite has recently been found to exhibit excellent impact resistance, inspired by the multi-hierarchical internal structures of the mantis shrimp's dactyl clubs. In previous work, our group demonstrated that via electrospinning-based additive manufacturing methodologies, weak polymer material constituents could be made to exhibit significantly improved toughness and impact properties. In this study, we demonstrate the use of 3D architected fiber-based polymer composites to protect the silicon solar cells by absorbing impact energy. The absorbed energy is equivalent to the energy that would impact the solar cells during hailstorms. We have shown that silicon cells placed under such 3D architected polymer layers break at substantially higher impact load/energy (compared to those placed under standard PV encapsulation polymer material). This could lead to the development of novel PV encapsulant materials for the next generation of lightweight PV modules and technology with excellent impact resistance.

Localized Probing of Dielectric Breakdown in Multilayer Hexagonal Boron Nitride.

Hexagonal boron nitride (h-BN) has emerged as a promising 2D/layered dielectric owing to its successful integration with graphene and other 2D materials, although a coherent picture of the overall dielectric breakdown mechanism in h-BN is yet to emerge. Here, we have carried out a systematic study using conduction atomic force microscopy to provide insights into the process of defect generation and dielectric degradation in the progressive breakdown (PBD) and hard breakdown (HBD) stages in 2-5 nm thick chemical vapor deposition (CVD)-grown multilayer h-BN films. The PBD and HBD regimes show different behaviors. Under electrical stress in the PBD stage, defects are generated progressively in the h-BN, leading to a gradual reduction of the effective barrier resistance and continuous soft breakdowns (SBDs) of the dielectric material. Random telegraph noise nano-spectroscopy shows that low frequency noise becomes dominant after an SBD event due to the creation of additional defects around the percolation path. We also observe a wide variation in the current-voltage (I-V) breakdown plots in the PBD stage, giving rise to non-Weibull statistical distribution of the breakdown voltage. We attribute this observation to the significant thickness inhomogeneity in the CVD films. At HBD, h-BN materials are always physically removed from the film, leading to the formation of pits at the breakdown location. Interestingly, pit formation is also occasionally observed in the PBD stage under very low current compliances, suggesting that breakdown may proceed by a mixture of defect generation and material removal in h-BN CVD films.

Helicoidally Arranged Polyacrylonitrile Fiber-Reinforced Strong and Impact-Resistant Thin Polyvinyl Alcohol Film Enabled by Electrospinning-Based Additive Manufacturing.

In this study, we demonstrate the use of parallel plate far field electrospinning (pp-FFES) based manufacturing system for the fabrication of polyacrylonitrile (PAN) fiber reinforced polyvinyl alcohol (PVA) strong polymer thin films (PVA SPTF). Parallel plate far field electrospinning (also known as the gap electrospinning) is generally used to produce uniaxially aligned fibers between the two parallel collector plates. In the first step, a disc containing PVA/H2O solution/bath (matrix material) was placed in between the two parallel plate collectors. Next, a layer of uniaxially aligned sub-micron PAN fibers (filler material) produced by pp-FFES was directly collected/embedded in the PVA/H2O solution by bringing the fibers in contact with the matrix. Next, the disc containing the matrix solution was rotated at 45∘ angular offset and then the next layer of the uniaxial fibers was collected/stacked on top of the previous layer with now 45∘ rotation between the two layers. This process was continued progressively by stacking the layers of uniaxially aligned arrays of fibers at 45∘ angular offsets, until a periodic pattern was achieved. In total, 13 such layers were laid within the matrix solution to make a helicoidal geometry with three pitches. The results demonstrate that embedding the helicoidal PAN fibers within the PVA enables efficient load transfer during high rate loading such as impact. The fabricated PVA strong polymer thin films with helicoidally arranged PAN fiber reinforcement (PVA SPTF-HA) show specific tensile strength 5 MPa · cm3· g-1 and can sustain specific impact energy (8 ± 0.9) mJ · cm3· g-1, which is superior to that of the pure PVA thin film (PVA TF) and PVA SPTF with randomly oriented PAN fiber reinforcement (PVA SPTF-RO). The novel fabrication methodology enables the further capability to produce even further smaller fibers (sub-micron down to even nanometer scales) and by the virtue of its layer-by-layer processing (in the manner of an additive manufacturing methodology) allowing further modulation of interfacial and inter-fiber adherence with the matrix materials. These parameters allow greater control and tunability of impact performances of the synthetic materials for various applications from army combat wear to sports and biomedical/wearable applications.

The effect of carbon nanotube chirality on the electrical conductivity of polymer nanocomposites considering tunneling resistance.

In this study, the effect on the conductance of polymer nanocomposites considering quantum tunneling resistance is investigated with respect to the chirality of carbon nanotubes (CNTs) and uncertainties in the geometric parameters of CNTs by using Monte Carlo simulations. The random spatial placement for CNTs was accomplished with a one-dimensional line segment and the periodic boundary conditions were applied to CNTs in the two-dimensional representative volume element. Intersection points between each CNT were calculated to obtain connectivity lists of the connected network path. Both the intrinsic resistance of the CNT and the inter-CNT tunneling resistance were considered in this model. In addition, the in-house code developed was validated by comparison with several experimental datasets from the literature. Unlike past studies, uncertainties in the chiral index of single-wall CNTs concerning armchair and zig-zag structures have been considered here and the electrical conductivity and percolation threshold are predicted.

Quantitative identification of technological paradigm changes using knowledge persistence.

This paper proposes a method to quantitatively identify the changes of technological paradigm over time. Specifically, the method identifies previous paradigms and predicts future paradigms by analyzing a patent citation-based knowledge network. The technological paradigm can be considered as dominantly important knowledge in a specific period. Therefore, we adopted the knowledge persistence which can quantify technological impact of an invention to recent technologies in a knowledge network. High knowledge persistence patents are dominant or paradigmatic inventions in a specific period and so changes of top knowledge persistence patents over time can show paradigm shifts. Moreover, since knowledge persistence of paradigmatic inventions are increasing dramatically faster than other ordinary inventions, recent patents having similar increasing trends in knowledge persistence with previous paradigms are identified as future paradigm inventions. We conducted an empirical case study using patents related to the genome sequencing technology. The results show that the identified previous paradigms are widely recognized as critical inventions in the domain by other studies and the identified future paradigms are also qualitatively significant inventions as promising technologies.

Fiber-Integrated Reversibly Wavelength-Tunable Nanowire Laser Based on Nanocavity Mode Coupling.

As an ideal miniaturized light source, wavelength-tunable nanolasers capable of emitting a wide spectrum stimulate intense interests for on-chip optoelectronics, optical communications, and spectroscopy. However, realization of such devices remains a major challenge because of extreme difficulties in achieving continuously reversibly tunable gain media and high quality (Q)-factor resonators on the nanoscale simultaneously. Here, exploiting single bandgap-graded CdSSe NWs and a Fabry-Pérot/whispering gallery mode (FP/WGM) coupling cavity, a free-standing fiber-integrated reversibly wavelength-tunable nanolaser covering a 42 nm wide spectrum at room temperature with high stability and reproducibility is demonstrated. In addition, a 1.13 nm tuning spectral resolution is realized. The substrate-free device design enables integration in optical fiber communications and information. With reversible and wide, continuous tunability of emission color and precise control per step, our work demonstrates a general approach to nanocavity coupling affording high Q-factors, enabling an ideal miniaturized module for a broad range of applications in optics and optoelectronics, with optical fiber integration.

Atomic Scale Modulation of Self-Rectifying Resistive Switching by Interfacial Defects.

Higher memory density and faster computational performance of resistive switching cells require reliable array-accessible architecture. However, selecting a designated cell within a crossbar array without interference from sneak path currents through neighboring cells is a general problem. Here, a highly doped n++ Si as the bottom electrode with Ni-electrode/HfO x /SiO2 asymmetric self-rectifying resistive switching device is fabricated. The interfacial defects in the HfO x /SiO2 junction and n++ Si substrate result in the reproducible rectifying behavior. In situ transmission electron microscopy is used to quantitatively study the properties of the morphology, chemistry, and dynamic nucleation-dissolution evolution of the chains of defects at the atomic scale. The spatial and temporal correlation between the concentration of oxygen vacancies and Ni-rich conductive filament modifies the resistive switching effect. This study has important implications at the array-level performance of high density resistive switching memories.

Fast response CdS-CdSxTe1-x-CdTe core-shell nanobelt photodetector.

Quasi-one-dimensional semiconductor nanostructure-based photodetectors show high sensitivity but suffer from slow response speed due to surface reaction. Here, we report a fast-response CdS-CdSxTe1-x-CdTe core-shell nanobelt photodetector with a rise time of 11 µs, which is the fastest among CdS based photodetectors reported previously. The improved response speed is ascribed to the suppressed possibilities of surface reaction resulting from the core-shell structure and heterojunction among the CdS, CdSxTe1-x and CdTe. The measured response spectrum of CdS-CdSxTe1-x-CdTe core-shell nanobelt photodetector covers a wide range from 355 to 785 nm. Moreover, high responsivity (1,520 A/W) and large 3 dB bandwidth (∼22.9 kHz) are obtained along with the fast response. The high performance in responsivity, sensitivity, spectral response and photoresponse speed makes this device a promising candidate for practical application in optical sensing, communication and imaging.

Using post-breakdown conduction study in a MIS structure to better understand the resistive switching mechanism in an MIM stack.

We apply our understanding of the physics of failure in the post-breakdown regime of high-κ dielectric-based conventional logic transistors having a metal-insulator-semiconductor (MIS) structure to interpret the mechanism of resistive switching in resistive random-access memory (RRAM) technology metal-insulator-metal (MIM) stacks. Oxygen vacancies, gate metal migration and metal filament formation in the gate dielectric which constitute the chemistry of breakdown in the post-breakdown stage of logic gate stacks are attributed to be the mechanisms responsible for the SET process in RRAM technology. In this paper, we draw an analogy between the breakdown study in logic devices and filamentation physics in resistive non-volatile memory.