A Novel Active Polyphase Filter Employing Frequency-Dependent Image Rejection Enhancement Technique.

In low intermediate frequency (low-IF) receivers, image interference rejection is one of the core tasks to be accomplished. Conventional active polyphase filters (APPFs) are unable to have a sufficient image rejection ratio (IRR) at high operating frequencies due to the degradation of the IRR by the amplitude and phase imbalances produced by the secondary pole. The proposed solution to the above problem is a frequency-dependent image rejection enhancement technique based on secondary pole compensation.

An Ultra-Low-Voltage Transconductance Stable and Enhanced OTA for ECG Signal Processing.

In this paper, a rail-to-rail transconductance stable and enhanced ultra-low-voltage operational transconductance amplifier (OTA) is proposed for electrocardiogram (ECG) signal processing. The variation regularity of the bulk transconductance of pMOS and nMOS transistors and the cancellation mechanism of two types of transconductance variations are revealed. On this basis, a transconductance stabilization and enhancement technique is proposed.

Polyfunctional and Multisensory Bio-Ionoelastomers Enabled by Covalent Adaptive Networks With Hierarchically Dynamic Bonding.

Developing versatile ionoelastomers, the alternatives to hydrogels and ionogels, will boost the advancement of high-performance ionotronic devices. However, meeting the requirements of bio-derivation, high toughness, high stretchability, autonomous self-healing ability, high ionic conductivity, reprocessing, and favorable recyclability in a single ionoelastomer remains a challenging endeavor. Herein, a dynamic covalent and supramolecular design, lipoic acid (LA)-based dynamic covalent ionoelastomer (DCIE), is proposed via melt building covalent adaptive networks with hierarchically dynamic bonding (CAN-HDB), wherein lithium bonds aid in the dissociation of ions and the integration of dynamic disulfide metathesis, lithium bonds, and binary hydrogen bonds enhances the mechanical performances, self-healing capability, reprocessing, and recyclability.

3D printable strong and tough composite organo-hydrogels inspired by natural hierarchical composite design principles.

Fabrication of composite hydrogels can effectively enhance the mechanical and functional properties of conventional hydrogels. While ceramic reinforcement is common in many hard biological tissues, ceramic-reinforced hydrogels lack a similar natural prototype for bioinspiration. This raises a key question: How can we still attain bioinspired mechanical mechanisms in composite hydrogels without mimicking a specific composition and structure? Abstracting the hierarchical composite design principles of natural materials, this study proposes a hierarchical fabrication strategy for ceramic-reinforced organo-hydrogels, featuring (1) aligned ceramic platelets through direct-ink-write printing, (2) poly(vinyl alcohol) organo-hydrogel matrix reinforced by solution substitution, and (3) silane-treated platelet-matrix interfaces.

Robust and sensitive conductive nanocomposite hydrogel with bridge cross-linking-dominated hierarchical structural design.

Conductive hydrogels have a remarkable potential for applications in soft electronics and robotics, owing to their noteworthy attributes, including electrical conductivity, stretchability, biocompatibility, etc. However, the limited strength and toughness of these hydrogels have traditionally impeded their practical implementation. Inspired by the hierarchical architecture of high-performance biological composites found in nature, we successfully fabricate a robust and sensitive conductive nanocomposite hydrogel through self-assembly-induced bridge cross-linking of MgB nanosheets and polyvinyl alcohol hydrogels.

Highly Robust Conductive Organo-Hydrogels with Powerful Sensing Capabilities Under Large Mechanical Stress.

The low mechanical strength of conductive hydrogels (<1 MPa) has been a significant hurdle in their practical application, as they are prone to fracturing under complex conditions, limiting their effectiveness. Here, this work fabricates a strong and tough conductive hierarchical poly(vinyl alcohol) (PEDOT:PSS/PVA) organo-hydrogel (PPS organo-hydrogel) via a facile combining strategy of self-assembly and stretch training. With PVA/PEDOT:PSS microlayers and aligned PVA/PEDOT:PSS nanofibers, PVA and PEDOT:PSS nanocrystalline domains, and semi-interpenetrating polymer networks, PPS organo-hydrogels display outstanding mechanical performances (strength: 54.

Customizable Resilient Multifunctional Graphene Aerogels via Blend-spinning assisted Freeze Casting.

Graphene aerogels have gained considerable attention due to their unique physical properties, but their poor mechanical properties and lack of functionality have hindered their advanced applications. In this study, we propose a blend-spinning-assisted freeze-casting (BSFC) strategy to incorporate particle-modified carbon fibers into graphene aerogels for mechanical strengthening and functional enhancement. This method offers a great deal of freedom in the creation of customizable multimaterial, multiscale structural graphene aerogels.

Prestrain Programmable 4D Printing of Nanoceramic Composites with Bioinspired Microstructure.

Four-dimensional (4D) printing enables programmable, predictable, and precise shape change of responsive materials to achieve desirable behaviors beyond conventional three-dimensional (3D) printing. However, applying 4D printing to ceramics remains challenging due to their intrinsic brittleness and inadequate stimuli-responsive ability. Here, this work proposes a conceptional combination of bioinspired microstructure design and a programmable prestrain approach for 4D printing of nanoceramics.

A note on factor normalization for deep neural network models.

Deep neural network (DNN) models often involve high-dimensional features. In most cases, these high-dimensional features can be decomposed into two parts: a low-dimensional factor and residual features with much-reduced variability and inter-feature correlation. This decomposition has several interesting theoretical implications for DNN training.