Size Scalability Research Articles

Size scalability of Monte Carlo simulations applied to oxidized polypyrrole systems

Oxidized polypyrrole (PPy) is a conducting polymer with diverse applications such as supercapacitors, sensors, batteries, actuators, neural prosthetics, among others. PPy is most commonly synthesized for the specific application yielding low molecular weight oligomers that form amorphous polymer matrices. Hence, molecular simulation analyses are challenging. This work generalizes the recently proposed coarse grained force field (CGFF) for halogen oxidized PPy in the condensed phases and introduces a novel implementation of the Monte Carlo (MC) simulation based on the CGFF that enables simulations of polymer systems with more than 100000 particles. The MC implementation utilizes a combination of CPU and GPUs and exploits a numerical approximation based on polynomial piecewise interpolation for the calculation of the CGFF pairwise additive terms. The MC simulations evidence that the oxidized PPy thermodynamic and structural properties are consistent as the system size is scaled up. Predicted properties include density, enthalpy, potential energy, heat capacity, coefficient of thermal expansion, caloric curve, glass transition temperature range, compressibility, bulk modulus, radial distribution functions, and polymer chain characteristics. The oxidized PPy samples display oligomer chain stacking that persists with temperatures up to the glass transition. Simulated properties are consistent with experimental observations when available and predict trends in all other cases.

Dynamic Electromechanical Co‐Stimulation Based Enhancement of Skeletal Muscle Tissues for Fast Biosyncretic Robots Actuation

AbstractBiosyncretic robots composed of living and synthetic materials have garnered significant attention due to their high energy conversion efficiency, good biocompatibility and human‐robot interaction safety. Among common living actuation materials, artificial skeletal muscle tissue (ASMT) stands out for its good size scalability, controllability, and potential high driving force. However, due to the low differentiation efficiency of myoblasts, the performance of ASMT lags behind that of natural skeletal muscle tissue, thereby hindering the progress of biosyncretic robots. Here, inspired by the training mode of human skeletal muscle, an electromechanical co‐stimulation system for enhancing the performance of ASMTs is proposed. This system is capable of simultaneously applying electrical and mechanical stimulation to ASMTs. Moreover, the mechanical resistance can be dynamically adjusted during ASMT growth based on real‐time measurements of the contractile force of the ASMT. The results show that the enhanced ASMTs demonstrate improved differentiation and performance and can actuate a robot at a maximum speed of 2.38 mm s−1, which is faster than those of most currently reported ASMT‐based robots. This study introduces a novel approach for enhancing the performance of ASMTs, with substantial implications for the fields of biosyncretic robots and tissue engineering.

GeSe ovonic threshold switch: the impact of functional layer thickness and device size

Three-dimensional phase change memory (3D PCM), possessing fast-speed, high-density and nonvolatility, has been successfully commercialized as storage class memory. A complete PCM device is composed of a memory cell and an associated ovonic threshold switch (OTS) device, which effectively resolves the leakage current issue in the crossbar array. The OTS materials are chalcogenide glasses consisting of chalcogens such as Te, Se and S as central elements, represented by GeTe6, GeSe and GeS. Among them, GeSe-based OTS materials are widely utilized in commercial 3D PCM, their scalability, however, has not been thoroughly investigated. Here, we explore the miniaturization of GeSe OTS selector, including functional layer thickness scalability and device size scalability. The threshold switching voltage of the GeSe OTS device almost lineally decreases with the thinning of the thickness, whereas it hardly changes with the device size. This indicates that the threshold switching behavior is triggered by the electric field, and the threshold switching field of the GeSe OTS selector is approximately 105 V/μm, regardless of the change in film thickness or device size. Systematically analyzing the threshold switching field of Ge–S and Ge–Te OTSs, we find that the threshold switching field of the OTS device is larger than 75 V/μm, significantly higher than PCM devices (8.1–56 V/μm), such as traditional Ge2Sb2Te5, Ag–In–Sb–Te, etc. Moreover, the required electric field is highly correlated with the optical bandgap. Our findings not only serve to optimize GeSe-based OTS device, but also may pave the approach for exploring OTS materials in chalcogenide alloys.

Ultralight, strong, and self-reprogrammable mechanical metamaterials.

Versatile programmable materials have long been envisioned that can reconfigure themselves to adapt to changing use cases in adaptive infrastructure, space exploration, disaster response, and more. We introduce a robotic structural system as an implementation of programmable matter, with mechanical performance and scale on par with conventional high-performance materials and truss systems. Fiber-reinforced composite truss-like building blocks form strong, stiff, and lightweight lattice structures as mechanical metamaterials. Two types of mobile robots operate over the exterior surface and through the interior of the system, performing transport, placement, and reversible fastening using the intrinsic lattice periodicity for indexing and metrology. Leveraging programmable matter algorithms to achieve scalability in size and complexity, this system design enables robust collective automated assembly and reconfiguration of large structures with simple robots. We describe the system design and experimental results from a 256-unit cell assembly demonstration and lattice mechanical testing, as well as a demonstration of disassembly and reconfiguration. The assembled structural lattice material exhibits ultralight mass density (0.0103 grams per cubic centimeter) with high strength and stiffness for its weight ( 11.38 kilopascals and 1.1129 megapascals, respectively), a material performance realm appropriate for applications like space structures. With simple robots and structure, high mass-specific structural performance, and competitive throughput, this system demonstrates the potential for self-reconfiguring autonomous metamaterials for diverse applications.

An extracellular matrix-mimicking magnetic microrobot for targeted elimination of circulating cancer cells.

Cancer cells disseminate through the bloodstream, leading to metastasis in distant sites within the body. One promising strategy to prevent metastasis is to eliminate circulating tumor cells. However, this remains challenging due to the lack of an active and targeted biomedical tool for efficient cancer cell elimination. Here, we developed a magnetic microrobot by using natural materials derived from the extracellular matrix (ECM) to mimic the ligand-receptor interaction between cancer cells and the ECM, offering targeted elimination of cancer cells. The ECM-mimicking microrobot is designed with a biodegradable hydrogel matrix, incorporating a cancer cell ligand and magnetic microparticles for cancer cell capture and active locomotion. This microrobot was fabricated based on an interface-shearing method, enabling controllable magnetic response and size scalability (30 μm-500 μm). The presented ECM-mimicking microrobot can actively approach and capture single cancer cells and cell clusters under the control of specific magnetic fields. The experiment was conducted in a blood vessel-mimicking simulator. The microrobot demonstrates an outstanding elimination efficacy of 92.3% on MDA-MB-231 cancer cells and a stable transport capability of the captured cells over long distances to a designed recycling site, inhibiting cell metastasis. This magnetic ECM-mimicking microrobot based on a bioinspired binding mechanism represents a promising candidate for the efficient elimination of cancer cells and other biological waste in the blood.

Mechanical Metamaterials Fabricated From Self-Assembly: A Perspective

Abstract Mechanical metamaterials, whose unique mechanical properties stem from their structural design rather than material constituents, are gaining popularity in engineering applications. In particular, recent advances in self-assembly techniques offer the potential to fabricate load-bearing mechanical metamaterials with unparalleled feature size control and scalability compared to those produced by additive manufacturing (AM). Yet, the field is still in its early stages. In this perspective, we first provide an overview of the state-of-the-art self-assembly techniques, with a focus on the copolymer and colloid crystal self-assembly processes. We then discuss current challenges and future opportunities in this research area, focusing on novel fabrication approaches, the need for high-throughput characterization methods, and the integration of Machine Learning (ML) and lab automation for inverse design. Given recent progress in all these areas, we foresee mechanical metamaterials fabricated from self-assembly techniques impacting a variety of applications relying on lightweight, strong, and tough materials.

Congestion control in constrained Internet of Things networks

AbstractThe Internet of Things (IoT) is a growing technology that remotely connects multiple devices (ranging across many fields and applications) over the Internet. The scalability of an IoT network mandates a reliable transport infrastructure. Traditional transport control protocol (TCP) control protocol is unsuitable for such domain, mainly due to energy and power consumption reasons. A lighter version of TCP, light weight IP (lwIP) provides a promising solution for current and projected future scalable IoT infrastructures. However, the original lwIP is just a simple mapping of the protocol, without insight into the IoT specific requirements. This paper examines the lwIP congestion control mechanism and addresses its shortcomings. In particular, a detailed examination is devoted to the various metrics such as retransmission time‐outs and its back‐off epochs, the congestion window behaviour and progress in the absence (and presence) of congestion. In particular, we propose a set of novel algorithms to address both the IoT constraints nature (light‐weight) as well as keeping up with scalability in IoT network size and performance. A detailed simulation study has been conducted to endorse the viability of our proposed set of algorithms for next‐generation IoT networks.

A flux growth technique for high quality cubic boron arsenide bulk single crystals

The growth of single crystal cubic boron arsenide (c-BAs) has attracted considerable interest due to its high room-temperature thermal conductivity and high ambipolar electrical mobility. However, currently the only growth technique reported for c-BAs crystals is the chemical vapor transport (CVT) method, which exhibits several drawbacks with regard to size scalability and crystal quality control, thereby hindering the further advancement of this semiconductor material. Herein, we report a flux growth technique using liquid arsenic (l-As) as a reaction medium at high pressures for the growth of high-quality c-BAs crystals with several millimeters size. The outstanding properties, including high uniformity, lower defect density, and lower carrier concentration of the as-grown c-BAs single crystals from flux growth, have been verified via a combination of techniques including x-ray diffraction, Raman scattering, photoluminescence spectroscopy, and electrical transport measurements, in comparison with the CVT-grown crystals.

Laser-driven hierarchical "gas-needles" for programmable and high-precision proximity transfer printing of microchips.

Micro-transfer printing (μTP) techniques are essential for advanced electronics. However, current contact/noncontact μTP techniques fail to simultaneously achieve high selectivity and transfer accuracy. Here, a laser projection proximity transfer (LaserPPT) technique is presented, which assembles the microchips in an approach-and-release manner, combining high-precision parallelism with individual chip control. An embedded carbon layer with a thin gas layer is generated by an ultraviolet laser, followed by absorbing heat from the infrared laser, to enable the sequential expansion of hierarchical "gas-needles." The level 1 large gas-needle with a substantially growing height can reduce the gap between the microchip and the receiver. Then, the level 2 small gas-needles enable the gentle release of a chip. Therefore, the LaserPPT can obtain a strong adhesion modulation (~1000 times), excellent size scalability (<100 micrometers), and high transfer accuracy of ~4 micrometers. Last, the assembly of a micro-light-emitting diode display demonstrates the capabilities for deterministic assembly of microarrays.

High-Throughput Mechanical Exfoliation for Low-Cost Production of van der Waals Nanosheets.

A method is presented for scaling up the production of flakes of van der Waals materials via mechanical exfoliation. Using a roll-to-roll setup and an automatized, massive parallel exfoliation process, adhesive tapes with a high density of nanosheets of van der Waals materials are produced. The technique allows for obtaining a good trade-off between large lateral size and excellent area scalability, while also maintaining low cost. The potential of the method is demonstrated through the successful fabrication of field effect transistors and flexible photodetectors in large batches. This low-cost method to produce large area films out of mechanically exfoliated flakes is very general, and it can be applied to a variety of substrates and van der Waals materials and, moreover, it can be used to combine different van der Waals materials on top of each other. Therefore, it is believed that this production method opens an interesting avenue for fabrication of low-cost devices while maintaining a good scalability and performance.

MCPNet: a parallel maximum capacity-based genome-scale gene network construction framework.

Gene network reconstruction from gene expression profiles is a compute- and data-intensive problem. Numerous methods based on diverse approaches including mutual information, random forests, Bayesian networks, correlation measures, as well as their transforms and filters such as data processing inequality, have been proposed. However, an effective gene network reconstruction method that performs well in all three aspects of computational efficiency, data size scalability, and output quality remains elusive. Simple techniques such as Pearson correlation are fast to compute but ignore indirect interactions, while more robust methods such as Bayesian networks are prohibitively time consuming to apply to tens of thousands of genes. We developed maximum capacity path (MCP) score, a novel maximum-capacity-path-based metric to quantify the relative strengths of direct and indirect gene-gene interactions. We further present MCPNet, an efficient, parallelized gene network reconstruction software based on MCP score, to reverse engineer networks in unsupervised and ensemble manners. Using synthetic and real Saccharomyces cervisiae datasets as well as real Arabidopsis thaliana datasets, we demonstrate that MCPNet produces better quality networks as measured by AUPRC, is significantly faster than all other gene network reconstruction software, and also scales well to tens of thousands of genes and hundreds of CPU cores. Thus, MCPNet represents a new gene network reconstruction tool that simultaneously achieves quality, performance, and scalability requirements. Source code freely available for download at https://doi.org/10.5281/zenodo.6499747 and https://github.com/AluruLab/MCPNet, implemented in C++ and supported on Linux.

Laser-assisted failure recovery for dielectric elastomer actuators in aerial robots.

Insects maintain remarkable agility after incurring severe injuries or wounds. Although robots driven by rigid actuators have demonstrated agile locomotion and manipulation, most of them lack animal-like robustness against unexpected damage. Dielectric elastomer actuators (DEAs) are a class of muscle-like soft transducers that have enabled nimble aerial, terrestrial, and aquatic robotic locomotion comparable to that of rigid actuators. However, unlike muscles, DEAs suffer local dielectric breakdowns that often cause global device failure. These local defects severely limit DEA performance, lifetime, and size scalability. We developed DEAs that can endure more than 100 punctures while maintaining high bandwidth (>400 hertz) and power density (>700 watt per kilogram)-sufficient for supporting energetically expensive locomotion such as flight. We fabricated electroluminescent DEAs for visualizing electrode connectivity under actuator damage. When the DEA suffered severe dielectric breakdowns that caused device failure, we demonstrated a laser-assisted repair method for isolating the critical defects and recovering performance. These results culminate in an aerial robot that can endure critical actuator and wing damage while maintaining similar accuracy in hovering flight. Our work highlights that soft robotic systems can embody animal-like agility and resilience-a critical biomimetic capability for future robots to interact with challenging environments.

Nonvolatile Ferroelectric LiNbO3 Domain Wall Crossbar Memory

High-density domain wall memory based on crossbar architecture is a strong contender among next-generation high performance versatile memories due to its ultra-fast operation speed and excellent size scalability. Herein, we report 4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 4 domain wall crossbar memory arrays fabricated on a LiNbO3 single crystal. Reversible creation and erasure of conducting domain walls between two antiparallel/parallel domains at bipolar write voltages enable the storage of digital “0” and “1” information. At the crosspoint of each word and bit lines, the memory cell can be accurately accessed, programmed and erased. The diode-like rectification of readout currents exhibited by all memory cells can inhibit read and write crosstalk. The electrical testing results in the crossbar memory array demonstrated the good stability of on/off currents and good uniformity of operating voltages, which can be reflected by the fact that the distribution of the coercive voltages is within 2.9 V and the “on”/ “off” current ratio is around 100 at a reading voltage of 3 V. Good data retention and fatigue resistance are also exhibited, making it possible to integrate domain wall random access memories in high density and reliability.

Observation of alumina nanoparticles generated from aqueous solutions of a "flat" aluminum-13 cluster.

Amorphous alumina nanoparticles were synthesized via dynamic processes during the dissolution of aluminum hydroxide by nitric acid, a method commonly used to produce aqueous solutions of aluminum oxide molecular clusters. These particles were characterized by DLS measurements, and corroborated by other solution and solid state analyses. The methods used represent a highly tuneable, facile synthetic pathway that allows for size targeting and scalability for industrial purposes, and provides insight into pH- and temperature-dependent alumina speciation and aggregation.

Metal-Organic Frameworks-Based Memristors: Materials, Devices, and Applications.

Facing the explosive growth of data, a number of new micro-nano devices with simple structure, low power consumption, and size scalability have emerged in recent years, such as neuromorphic computing based on memristor. The selection of resistive switching layer materials is extremely important for fabricating of high performance memristors. As an organic-inorganic hybrid material, metal-organic frameworks (MOFs) have the advantages of both inorganic and organic materials, which makes the memristors using it as a resistive switching layer show the characteristics of fast erasing speed, outstanding cycling stability, conspicuous mechanical flexibility, good biocompatibility, etc. Herein, the recent advances of MOFs-based memristors in materials, devices, and applications are summarized, especially the potential applications of MOFs-based memristors in data storage and neuromorphic computing. There also are discussions and analyses of the challenges of the current research to provide valuable insights for the development of MOFs-based memristors.

Higher order neural processing with input-adaptive dynamic weights on MoS2 memtransistor crossbars

The increasing complexity of deep learning systems has pushed conventional computing technologies to their limits. While the memristor is one of the prevailing technologies for deep learning acceleration, it is only suited for classical learning layers where only two operands, namely weights and inputs, are processed simultaneously. Meanwhile, to improve the computational efficiency of deep learning for emerging applications, a variety of non-traditional layers requiring concurrent processing of many operands are becoming popular. For example, hypernetworks improve their predictive robustness by simultaneously processing weights and inputs against the application context. Two-electrode memristor grids cannot directly map emerging layers’ higher-order multiplicative neural interactions. Addressing this unmet need, we present crossbar processing using dual-gated memtransistors based on two-dimensional semiconductor MoS2. Unlike the memristor, the resistance states of memtransistors can be persistently programmed and can be actively controlled by multiple gate electrodes. Thus, the discussed memtransistor crossbar enables several advanced inference architectures beyond a conventional passive crossbar. For example, we show that sneak paths can be effectively suppressed in memtransistor crossbars, whereas they limit size scalability in a passive memristor crossbar. Similarly, exploiting gate terminals to suppress crossbar weights dynamically reduces biasing power by ∼20% in memtransistor crossbars for a fully connected layer of AlexNet. On emerging layers such as hypernetworks, collocating multiple operations within the same crossbar cells reduces operating power by ∼15× on the considered network cases.

Performance analysis of relaxation Runge–Kutta methods

Recently, global and local relaxation Runge–Kutta methods have been developed for guaranteeing the conservation, dissipation, or other solution properties for general convex functionals whose dynamics are crucial for an ordinary differential equation solution. These novel time integration procedures have an application in a wide range of problems that require dynamics-consistent and stable numerical methods. The application of a relaxation scheme involves solving scalar nonlinear algebraic equations to find the relaxation parameter. Even though root-finding may seem to be a problem technically straightforward and computationally insignificant, we address the problem at scale as we solve full-scale industrial problems on a CPU-powered supercomputer and show its cost to be considerable. In particular, we apply the relaxation schemes in the context of the compressible Navier–Stokes equations and use them to enforce the correct entropy evolution. We use seven different algorithms to solve for the global and local relaxation parameters and analyze their strong scalability. As a result of this analysis, within the global relaxation scheme, we recommend using Brent’s method for problems with a low polynomial degree and of small sizes for the global relaxation scheme, while secant proves to be the best choice for higher polynomial degree solutions and large problem sizes. For the local relaxation scheme, we recommend secant. Further, we compare the schemes’ performance using their most efficient implementations, where we look at their effect on the timestep size, overhead, and weak scalability. We show the global relaxation scheme to be always more expensive than the local approach—typically 1.1–1.5 times the cost. At the same time, we highlight scenarios where the global relaxation scheme might underperform due to its increased communication requirements. Finally, we present an analysis that sets expectations on the computational overhead anticipated based on the system properties.

Femtosecond Laser-Fabricated Photonic Chips for Optical Communications: A Review

Integrated optics, having the unique properties of small size, low loss, high integration, and high scalability, is attracting considerable attention and has found many applications in optical communications, fulfilling the requirements for the ever-growing information rate and complexity in modern optical communication systems. Femtosecond laser fabrication is an acknowledged technique for producing integrated photonic devices with unique features, such as three-dimensional fabrication geometry, rapid prototyping, and single-step fabrication. Thus, plenty of femtosecond laser-fabricated on-chip devices have been manufactured to realize various optical communication functions, such as laser generation, laser amplification, laser modulation, frequency conversion, multi-dimensional multiplexing, and photonic wire bonding. In this paper, we review some of the most relevant research progress in femtosecond laser-fabricated photonic chips for optical communications, which may break new ground in this area. First, the basic principle of femtosecond laser fabrication and different types of laser-inscribed waveguides are briefly introduced. The devices are organized into two categories: active devices and passive devices. In the former category, waveguide lasers, amplifiers, electric-optic modulators, and frequency converters are reviewed, while in the latter, polarization multiplexers, mode multiplexers, and fan-in/fan-out devices are discussed. Later, photonic wire bonding is also introduced. Finally, conclusions and prospects in this field are also discussed.

Luminescent solar concentrator based on large-Stokes shift tetraphenylpyrazine fluorophore combining aggregation-induced emission and intramolecular charge transfer features

This study presents bulk configuration large-area luminescent solar concentrators (LSCs) incorporating a novel organic fluorophore (TPP1) based on tetraphenylpyrazine core symmetrically functionalized with electron-rich dimethylamine (donor) and electron-deficient cyano (acceptor) moieties. As confirmed experimentally and validated through a computational approach, TPP1 exhibits aggregation-induced emission and a large Stokes shift due to intramolecular charge transfer, making it an excellent candidate as a fluorophore in poly(methyl methacrylate) based LSCs. Based on promising photophysical properties, TPP1 LSCs with suitable concentration showed an internal and external photon efficiency of 23.7% and 2.33% under AM 1.5G illumination, respectively. The size scalability (up to 100 cm length) of TPP1 LSCs was also evaluated by employing analytical models. Moreover, the power conversion efficiency of 10 × 10 × 0.3 cm3 LSC (geometrical factor 8.33) was found to be 0.66% and 0.34%, with and without scattering background, respectively. Such outstanding optical and PV performances of TPP1 LSCs clearly illustrate that TPP1 with combined AIE and ICT features is a viable alternative to most organic dyes. Additionally, stability and aesthetics analysis of TPP1 LSCs also suggest their long-term use and compatibility with the built environment.

Efficient quantum dot light-emitting diodes with ultra-homogeneous and highly ordered quantum dot monolayer

Regarding conventional quantum dot light-emitting diodes (QLEDs) fabricated by using the spin-coating (SC) technique, voids and interstitial spaces are inevitable due to unordered quantum dots (QDs) stacking, generating device leakage current under an external bias. In the present study, we fabricated an ultra-homogeneous and highly ordered QD monolayer by adopting the Langmuir-Blodgett (LB) technique. The QD monolayer was transferred as a emissive layer with a horizontal lifting (HL) method to a red QLED, which exhibited high performance with an external quantum efficiency (EQE) of 19.0% and lifetime (T95@100 cd m−2) of 13,324 h. When compared with the SC-based device, the EQE and lifetime were improved by 15% and 183% due to the compact and ordered QD monolayer that lowered the leakage current. Moreover, white QLEDs with stacked QD monolayers could be obtained at a low voltage of 4 V because LB technique is an organic-solvent-free approach avoiding interlayer mixing and controlling the QD layer thickness precisely. In addition, we successfully fabricated an ultra-homogeneous large-area QD monolayer on a rectangular substrate with a size of 9 cm × 5 cm, indicating the promising size scalability of the LB-HL strategy.