The correlation of serum musclin with diabetic nephropathy.

OBJECTIVE: Musclin is a recently found myokine involved in the process of glucose metabolism. The purpose of the present investigation is to evaluate the relationship between serum musclin levels and diabetic nephropathy (DN). METHODS: The current investigation included 175 (T2DM) cases and 62 controls. T2DM patients were divided into three subgroups: normoalbuminuria (DN0), microalbuminuria (DN1), and macroalbuminuria (DN2) on the basis of the values of urine albumin to creatinine ratio (ACR). RESULTS: T2DM group displayed higher serum musclin than the controls. Serum musclin were remarkably elevated in DN2 subgroup compared with DN0 and DN1 subgroups. In addition, elevated serum musclin was observed in DN1 subgroup than in the DN0 subgroup. Serum musclin was correlated with an increased risk of having T2DM and DN using a logistic regression model. Linear regression analysis showed that serum musclin was negatively related with gender, and positively related with body mass index, systolic blood pressure, blood urea nitrogen, creatinine, and ACR. CONCLUSION: Serum musclin increases with the progressed stages of DN. Serum musclin is associated with renal function parameters and ACR.

Antimony as a Programmable Element in Integrated Nanophotonics.

The use of nonlinear elements with memory as photonic computing components has seen a huge surge in interest in recent years with the rise of artificial intelligence and machine learning. A key component is the nonlinear element itself. A class of materials known as phase change materials has been extensively used to demonstrate the viability of such computing. However, such materials continue to have relatively slow switching speeds, and issues with cyclability related to phase segregation of phase change alloys. Here, using antimony (Sb) thin films with thicknesses less than 5 nm we demonstrate reversible, ultrafast switching on an integrated photonic platform with retention time of tens of seconds. We use subpicosecond pulses, the shortest used to switch such elements, to program seven distinct memory levels. This portends their use in ultrafast nanophotonic applications ranging from nanophotonic beam steerers to nanoscale integrated elements for photonic computing.

A Universal Pick-and-Place Assembly for Nanowires.

With the introduction of techniques to grow highly functional nanowires of exotic materials and demonstrations of their potential in new applications, techniques for depositing nanowires on functional platforms have been an area of active interest. However, difficulties in handling individual nanowires with high accuracy and reliability have so far been a limiting factor in large-scale integration of high-quality nanowires. Here, a technique is demonstrated to transfer single nanowires reliably on virtually any platform, under ambient conditions. Functional nanowires of InP, AlGaAs, and GeTe on various patterned structures such as electrodes, nanophotonic devices, and even ultrathin transmission electron microscopy (TEM) membranes are transferred. It is shown that the versatility of this technique further enables to perform on-chip nano-optomechanical measurements of an InP nanowire for the first time via evanescent field coupling. Thus, this technique facilitates effortless integration of single nanowires into applications that were previously seen as cumbersome or even impractical, spanning a wide range from TEM studies to in situ electrical, optical, and mechanical characterization.

Optical and optoelectronic neuromorphic devices based on emerging memory technologies.

As artificial intelligence continues its rapid development, inevitable challenges arise for the mainstream computing hardware to process voluminous data (Big data). The conventional computer system based on von Neumann architecture with separated processor unit and memory is approaching the limit of computational speed and energy efficiency. Thus, novel computing architectures such as in-memory computing and neuromorphic computing based on emerging memory technologies have been proposed. In recent years, light is incorporated into computational devices, beyond the data transmission in traditional optical communications, due to its innate superiority in speed, bandwidth, energy efficiency, etc. Thereinto, photo-assisted and photoelectrical synapses are developed for neuromorphic computing. Additionally, both the storage and readout processes can be implemented in optical domain in some emerging photonic devices to leverage unique properties of photonics. In this review, we introduce typical photonic neuromorphic devices rooted from emerging memory technologies together with corresponding operational mechanisms. In the end, the advantages and limitations of these devices originated from different modulation means are listed and discussed.

Roadmap on emerging hardware and technology for machine learning.

Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.

Dynamically tunable transmissive color filters using ultra-thin phase change materials.

Structural color filters (i.e. plasmonics and nano-cavities) provide vivid and robust color filtering in applications such as CMOS image sensors but lack simplicity in fabrication and dynamic tuning. Here we report a dynamically tunable, transmissive color filter by incorporating an ultra-thin phase change layer inside a thin-film optical resonator. The transmitted color spectrum can be designed over the entire visible range and shifted by around 50 nm after phase transition. Angle dependence shows little color variation within a ±30° viewing angle. Crucially, only film deposition is required to fabricate our phase change color filter, showing great potential for large-scale and inexpensive production. The dynamically tunable color filter, described in this paper, could be a promising component in display, CMOS sensor, and solar cell technology.

Strong Opto-Structural Coupling in Low Dimensional GeSe3 Films.

Chalcogenide glasses as nanoscale thin films have become leading candidates for several optical and photonic technologies, ranging from reflective displays and filters to photonic memories. Current material systems, however, show strong optical absorption which limits their performance efficiencies and complicates device level integration. Herein, we report sputter deposited thin films of GeSe3, which are low loss and in which the flexible nature of the atomic structure results in thermally activated tunability in the refractive index as well as in the film's physical volume. Such changes, which occur beyond a threshold temperature are observed to be accumulative and directed toward a more equilibrium amorphous state of the film, instead of crystallization. Our results provide insight into a new type of configurability that is based on strong coupling in the material's opto-structural properties. The low optical losses in this material system combined with the tunability in the optical properties in the visible and near-infrared have direct application in higher performing optical coatings and in corrective optics.

Sub-10-nm intracellular bioelectronic probes from nanowire-nanotube heterostructures.

The miniaturization of bioelectronic intracellular probes with a wide dynamic frequency range can open up opportunities to study biological structures inaccessible by existing methods in a minimally invasive manner. Here, we report the design, fabrication, and demonstration of intracellular bioelectronic devices with probe sizes less than 10 nm. The devices are based on a nanowire-nanotube heterostructure in which a nanowire field-effect transistor detector is synthetically integrated with a nanotube cellular probe. Sub-10-nm nanotube probes were realized by a two-step selective etching approach that reduces the diameter of the nanotube free-end while maintaining a larger diameter at the nanowire detector necessary for mechanical strength and electrical sensitivity. Quasi-static water-gate measurements demonstrated selective device response to solution inside the nanotube, and pulsed measurements together with numerical simulations confirmed the capability to record fast electrophysiological signals. Systematic studies of the probe bandwidth in different ionic concentration solutions revealed the underlying mechanism governing the time response. In addition, the bandwidth effect of phospholipid coatings, which are important for intracellular recording, was investigated and modeled. The robustness of these sub-10-nm bioelectronics probes for intracellular interrogation was verified by optical imaging and recording the transmembrane resting potential of HL-1 cells. These ultrasmall bioelectronic probes enable direct detection of cellular electrical activity with highest spatial resolution achieved to date, and with further integration into larger chip arrays could provide a unique platform for ultra-high-resolution mapping of activity in neural networks and other systems.

Sensitivity limits and scaling of bioelectronic graphene transducers.

Semiconducting nanomaterials are being intensively studied as active elements in bioelectronic devices, with the aim of improving spatial resolution. Yet, the consequences of size-reduction on fundamental noise limits, or minimum resolvable signals, and their impact on device design considerations have not been defined. Here, we address these key issues by quantifying the size-dependent performance and limiting factors of graphene (Gra) transducers under physiological conditions. We show that suspended Gra devices represent the optimal configuration for cardiac extracellular electrophysiology in terms of both transducer sensitivity, systematically ~5× higher than substrate-supported devices, and forming tight bioelectronic interfaces. Significantly, noise measurements on free-standing Gra together with theoretical calculations yield a direct relationship between low-frequency 1/f noise and water dipole-induced disorders, which sets fundamental sensitivity limits for Gra devices in physiological media. As a consequence, a square-root-of-area scaling of Gra transducer sensitivity was experimentally revealed to provide a critical design rule for their implementation in bioelectronics.

Neuromorphic Photonic Memory Devices Using Ultrafast, Non-Volatile Phase-Change Materials.

The search for ultrafast photonic memory devices is inspired by the ever-increasing number of cloud-computing, supercomputing, and artificial-intelligence applications, together with the unique advantages of signal processing in the optical domain such as high speed, large bandwidth, and low energy consumption. By embracing silicon photonics with chalcogenide phase-change materials (PCMs), non-volatile integrated photonic memory is developed with promising potential in photonic integrated circuits and nanophotonic applications. While conventional PCMs suffer from slow crystallization speed, scandium-doped antimony telluride (SST) has been recently developed for ultrafast phase-change random-access memory applications. An ultrafast non-volatile photonic memory based on an SST thin film with a 2 ns write/erase speed is demonstrated, which is the fastest write/erase speed ever reported in integrated phase-change photonic devices. SST-based photonic memories exhibit multilevel capabilities and good stability at room temperature. By mapping the memory level to the biological synapse weight, an artificial neural network based on photonic memory devices is successfully established for image classification. Additionally, a reflective nanodisplay application using SST with optoelectronic modulation capabilities is demonstrated. Both the optical and electrical changes in SST during the phase transition and the fast-switching speed demonstrate their potential for use in photonic computing, neuromorphic computing, nanophotonics, and optoelectronic applications.

Toward intrinsic graphene surfaces: a systematic study on thermal annealing and wet-chemical treatment of SiO2-supported graphene devices.

By combining atomic force microscopy and trans-port measurements, we systematically investigated effects of thermal annealing on surface morphologies and electrical properties of single-layer graphene devices fabricated by electron beam lithography on silicon oxide (SiO(2)) substrates. Thermal treatment above 300 °C in vacuum was required to effectively remove resist residues on graphene surfaces. However, annealing at high temperature was found to concomitantly bring graphene in close contact with SiO(2) substrates and induce increased coupling between them, which leads to heavy hole doping and severe degradation of mobilities in graphene devices. To address this problem, a wet-chemical approach employing chloroform was developed in our study, which was shown to enable both intrinsic surfaces and enhanced electrical properties of graphene devices. Upon the recovery of intrinsic surfaces of graphene, the adsorption and assisted fibrillation of amyloid ß-peptide (Aß1-42) on graphene were electrically measured in real time.

Suspended graphene sensors with improved signal and reduced noise.

We report enhanced performance of suspended graphene field effect transistors (Gra-FETs) as sensors in aqueous solutions. Etching of the silicon oxide (SiO(2)) substrate underneath graphene was carried out in situ during electrical measurements of devices, which enabled systematic comparison of transport properties for same devices before and after suspension. Significantly, the transconductance of Gra-FETs in the linear operating modes increases 1.5 and 2 times when the power of low-frequency noise concomitantly decreases 12 and 6 times for hole and electron carriers, respectively, after suspension of graphene in solution from the SiO(2) substrate. Suspended graphene devices were further demonstrated as direct and real-time pH sensors, and complementary pH sensing with the same nanodevice working as either a p-type or n-type transistor was experimentally realized by offsetting the electrolyte gate potential in solution. Our results highlight the importance to quantify fundamental parameters that define resolution of graphene-based bioelectronics and demonstrate that suspended nanodevices represent attractive platforms for chemical and biological sensors.

Self-assembled 1-octadecanethiol monolayers on graphene for mercury detection.

We report studies on surface modification of graphene with 1-octadecanethiol and its application as heavy metal sensors. The alkanethiol molecules can self-assemble into large-scale highly ordered monolayers on single-layer graphene regardless of the roughness of graphene surfaces inherited from the underlying amorphorous silicon oxide (SiO2) dielectric substrates. Atomically resolved scanning tunneling microscopy imaging of modified graphene sheets on SiO2 was conducted to reveal configuration details of the self-assembled structure. Functionalization of graphene field effect transistors (Gra-FETs) with 1-octadecanethiol was realized and successfully explored for mercury(II) (Hg2+) detection at 10 ppm.

Antimony thin films demonstrate programmable optical nonlinearity.

The use of metals of nanometer dimensions to enhance and manipulate light-matter interactions for emerging plasmonics-enabled nanophotonic and optoelectronic applications is an interesting yet not highly explored area of research beyond plasmonics. Even more importantly, the concept of an active metal that can undergo an optical nonvolatile transition has not been explored. Here, we demonstrate that antimony (Sb), a pure metal, is optically distinguishable between two programmable states as nanoscale thin films. We show that these states, corresponding to the crystalline and amorphous phases of the metal, are stable at room temperature. Crucially from an application standpoint, we demonstrate both its optoelectronic modulation capabilities and switching speed using single subpicosecond pulses. The simplicity of depositing a single metal portends its potential for use in any optoelectronic application where metallic conductors with an actively tunable state are important.

Spontaneous formation of nanostructures in graphene.

We report in-depth studies of nanostructures formed in graphene on soft substrates. Periodic buckles with amplitude of nanometer scale spontaneously appear at edges of single-layer membranes after cooling of samples from above the substrate's glass-transition temperature. Stress modulation at step-edges between single- and few-layer further induces penetrating nanobuckles into the few-layer. The evolvement of single-layer folding into double and triple-layer stacks at elevated temperature was also probed in detail, and we show that the developed interfaces are clear of polymer contamination. Our results underscore the possibility to construct diverse nanostructures and to design novel devices based on graphene.

In-memory computing on a photonic platform.

Collocated data processing and storage are the norm in biological computing systems such as the mammalian brain. As our ability to create better hardware improves, new computational paradigms are being explored beyond von Neumann architectures. Integrated photonic circuits are an attractive solution for on-chip computing which can leverage the increased speed and bandwidth potential of the optical domain, and importantly, remove the need for electro-optical conversions. Here we show that we can combine integrated optics with collocated data storage and processing to enable all-photonic in-memory computations. By employing nonvolatile photonic elements based on the phase-change material, Ge2Sb2Te5, we achieve direct scalar and matrix-vector multiplication, featuring a novel single-shot Write/Erase and a drift-free process. The output pulse, carrying the information of the light-matter interaction, is the result of the computation. Our all-optical approach is novel, easy to fabricate and operate, and sets the stage for development of entirely photonic computers.

Plasmonic nanogap enhanced phase-change devices with dual electrical-optical functionality.

Modern-day computers rely on electrical signaling for the processing and storage of data, which is bandwidth-limited and power hungry. This fact has long been realized in the communications field, where optical signaling is the norm. However, exploiting optical signaling in computing will require new on-chip devices that work seamlessly in both electrical and optical domains, without the need for repeated electrical-to-optical conversion. Phase-change devices can, in principle, provide such dual electrical-optical operation, but assimilating both functionalities into a single device has so far proved elusive owing to conflicting requirements of size-limited electrical switching and diffraction-limited optical response. Here, we combine plasmonics, photonics, and electronics to deliver an integrated phase-change memory cell that can be electrically or optically switched between binary or multilevel states. Crucially, this device can also be simultaneously read out both optically and electrically, offering a new strategy for merging computing and communications technologies.

Device-Level Photonic Memories and Logic Applications Using Phase-Change Materials.

Inspired by the great success of fiber optics in ultrafast data transmission, photonic computing is being extensively studied as an alternative to replace or hybridize electronic computers, which are reaching speed and bandwidth limitations. Mimicking and implementing basic computing elements on photonic devices is a first and essential step toward all-optical computers. Here, an optical pulse-width modulation (PWM) switching of phase-change materials on an integrated waveguide is developed, which allows practical implementation of photonic memories and logic devices. It is established that PWM with low peak power is very effective for recrystallization of phase-change materials, in terms of both energy efficiency and process control. Using this understanding, multilevel photonic memories with complete random accessibility are then implemented. Finally, programmable optical logic devices are demonstrated conceptually and experimentally, with logic "OR" and "NAND" achieved on just a single integrated photonic phase-change cell. This study provides a practical and elegant technique to optically program photonic phase-change devices for computing applications.

On-chip photonic synapse.

The search for new "neuromorphic computing" architectures that mimic the brain's approach to simultaneous processing and storage of information is intense. Because, in real brains, neuronal synapses outnumber neurons by many orders of magnitude, the realization of hardware devices mimicking the functionality of a synapse is a first and essential step in such a search. We report the development of such a hardware synapse, implemented entirely in the optical domain via a photonic integrated-circuit approach. Using purely optical means brings the benefits of ultrafast operation speed, virtually unlimited bandwidth, and no electrical interconnect power losses. Our synapse uses phase-change materials combined with integrated silicon nitride waveguides. Crucially, we can randomly set the synaptic weight simply by varying the number of optical pulses sent down the waveguide, delivering an incredibly simple yet powerful approach that heralds systems with a continuously variable synaptic plasticity resembling the true analog nature of biological synapses.

Syringe-injectable electronics.

Seamless and minimally invasive three-dimensional interpenetration of electronics within artificial or natural structures could allow for continuous monitoring and manipulation of their properties. Flexible electronics provide a means for conforming electronics to non-planar surfaces, yet targeted delivery of flexible electronics to internal regions remains difficult. Here, we overcome this challenge by demonstrating the syringe injection (and subsequent unfolding) of sub-micrometre-thick, centimetre-scale macroporous mesh electronics through needles with a diameter as small as 100 µm. Our results show that electronic components can be injected into man-made and biological cavities, as well as dense gels and tissue, with >90% device yield. We demonstrate several applications of syringe-injectable electronics as a general approach for interpenetrating flexible electronics with three-dimensional structures, including (1) monitoring internal mechanical strains in polymer cavities, (2) tight integration and low chronic immunoreactivity with several distinct regions of the brain, and (3) in vivo multiplexed neural recording. Moreover, syringe injection enables the delivery of flexible electronics through a rigid shell, the delivery of large-volume flexible electronics that can fill internal cavities, and co-injection of electronics with other materials into host structures, opening up unique applications for flexible electronics.