The use of collaborative robots (Cobots) for materials development in chemical laboratories is currently of high priority. Herein, the Cobot is used for autonomous continued analysis and synthesis of graphene oxide–polyethyleneimine‐based membrane to unify a method and prospects for big data collection are shown. Membranes have already demonstrated a selective affinity to potassium cations and promised to adjust permeability for other cations by changing pH. The Cobot allows a variation of membrane properties by its composition modification. The present strategy combines a novel perspective of material production by Cobots and the application of machine learning. Moreover, the current approach can be adapted for different modern chemical laboratories for various scientific research, and the proper workflow is provided.

Visible light excited room temperature afterglow luminescent carbon dots are rare and most of them are excited by ultraviolet light, which has potential phototoxicity, and the quantum yield of afterglow is always low. This article reports a novel room temperature afterglow luminescent carbon dots/boric acid composite (BCDs composite) that can be excited by visible light such as 450 nm LED light, flashlight, and even mobile phone flashlight with high afterglow quantum yield of 48.4 % and long afterglow lifetime of 866 ms. Based on their excellent afterglow emission property, they can be applied for information security under humidity stimulation. Under daily storage condition, the information written by BCDs composite has no afterglow, and when dried with a hair dryer, the information was lighted by afterglow. Meanwhile, the BCDs composite can also be used for highly sensitivity Fe3+ sensing with a low detection limit of 102 nM in Tris-HCl buffer. This study not only successfully developed a novel room temperature long afterglow luminescent carbon dot based nanomaterial with multiple uses and visible light excitation, but also may provide ideas for the design of subsequent room temperature afterglow carbon dots and carbon dot based sensing probes.

Graphene oxide (GO) based multi-layered membranes have shown outstanding molecular-sieving properties for gas separation, surpassing the upper bound for polymeric membranes especially for hydrogen decarbonization. At the same time, the mechanism of gas permeation through such 2D GO membranes is very different in comparison to the traditional polymeric membranes due to multilayer, laminated nature of the former. For strategical design of novel membranes based on two-dimensional materials, it is important to understand the mechanism and to be able to measure two key parameters for gas transport: diffusivity and solubility. Such measurements are well established for the characterization of transport properties of bulk polymeric membranes. However, it is still a challenge to measure gas diffusion coefficients directly and accurately in ultra-thin multi-layered membranes. The lack of characterization limits our understanding of the mechanisms of gas transport though such membranes. In this work, we applied a time-lag method to determine the diffusivities for He, H2, O2, N2, CH4, CO2 and H2/CO2 equimolar mixture by on-line mass spectrometry. In contrast to polymeric membranes, the diffusivity and diffusion activation energy for all gases in 2D membranes are exponentially dependent on pathway length. Thus, in 2D membrane we can use an easy strategy to precisely regulate permeability and selectivity by the adjustment of the number of nanolayers and the size of 2D flakes, which is not possible using traditional polymeric membranes. This study is important for both the characterization and the standardization of gas transport properties of multi-layered membranes, and the design of novel membranes based on 2D materials.

Intelligent hydrogels have been paid a great deal of attention by worldwide researchers because of their ability to mimic biological functions. Communication, learning, and adaptation to the environment are important properties of living matter that are extremely difficult to mimic using artificial materials and technologies. Hydrogels are unique biomimetic materials. Because of the high water content and the cross-linked polymeric network, hydrogels can mimic physiological intracellular crowdedness, biointerfaces, and extracellular environments. Composite hydrogels are multifunctional stimuli-responsive materials. The switch between different functionalities can be regulated by the electric field, magnetic field, ultrasound, temperature, chemical parameters (such as pH and ionic strength), and biological stimuli (products of metabolic cycles). Hydrogel-based devices that are capable of operating with ionic currents are used to create machine-body interfaces, smart membranes, soft robotics and bionics, intelligent water treatment, and so on. In this chapter, we summarize the latest achievements of composite hydrogels to perform multiple and complex biomimetic functions for the design of novel bioinspired energy-related devices, smart catalysis, intelligent membranes, biocompatible actuators, and biomedical engineering.

Accurate human motion tracking by wearable sensors is critical for wearable robots in rehabilitation, but existing sensing technologies have several limitations, such as unaffordability, poor stability, and reliability concerns. Through the sensor morphology design, this research focuses on the development of robust soft strain sensors using crumpled single-walled carbon nanotubes (SWCNTs). Compared to planar sensors, crumpled SWCNTs sensors exhibit wide working strain ranges, robust cycling performance, and superior mechanical stability. These sensors were integrated into a rehabilitation exoskeleton and successfully monitored elbow deformation and muscle activity by sensitive, stable, and reliable signals, indicating great potential in replacing EMG and inertial sensors to provide accurate and immediate feedback for optimized operations in rehabilitation tasks. This technology provides a cost-effective, wearable, and privacy-friendly solution for motion monitoring in rehabilitation robots, improving the effectiveness and convenience of rehabilitation treatment for people with physical disabilities.

Novel 2D hydrogels with coupled electro‐thermoregulation for water transport are constructed via self‐assembly of hydroxypropyl cellulose (HPC) with graphene oxide (GO) and reduced GO (rGO), where the capability of HPC to switch between hydrophobic and hydrophilic states is exploited. Amphiphilic GO surface is used to guide the ordering and alignment of liquid crystalline HPC domains. The changes in conformation and alignment of HPC serve as a switch for optical properties and water transport. A composite consisting of electrically conductive rGO and 2D hydrogel is developed to produce thermal stimulus and thus construct an electro‐thermo controlled valve for regulated water transfer. The prepared 2D hydrogel has a large swelling rate, outstanding mechanical properties (Young's modulus is 2.5 GPa), and superior electrical conductivity (176 S cm−1). The ability of HPC domains to change conformation in 2D confinement, when Joule heating is applied, can function as a 2D low footprint water switcher with optical control in smart membranes. The proposed sustainable approach to self‐assembly of HPC in 2D confinement of GO and rGO is applicable to the whole family of lower critical solution temperature polymers. Thus, a ubiquitous and sustainable synthesis of novel low dimensional robust multifunctional hydrogels is demonstrated.

Wearable strain sensors that detect joint/muscle strain changes become prevalent at human–machine interfaces for full-body motion monitoring. However, most wearable devices cannot offer customizable opportunities to match the sensor characteristics with specific deformation ranges of joints/muscles, resulting in suboptimal performance. Adequate wearable strain sensor design is highly required to achieve user-designated working windows without sacrificing high sensitivity, accompanied with real-time data processing. Herein, wearable Ti 3 C 2 T x MXene sensor modules are fabricated with in-sensor machine learning (ML) models, either functioning via wireless streaming or edge computing, for full-body motion classifications and avatar reconstruction. Through topographic design on piezoresistive nanolayers, the wearable strain sensor modules exhibited ultrahigh sensitivities within the working windows that meet all joint deformation ranges. By integrating the wearable sensors with a ML chip, an edge sensor module is fabricated, enabling in-sensor reconstruction of high-precision avatar animations that mimic continuous full-body motions with an average avatar determination error of 3.5 cm, without additional computing devices.

Hyperbolic paraboloid surfaces or saddle-shaped materials can exist in two equilibrium shapes when the saddle shape reverses on itself and, therefore, can be used as structural elements of new stimuli-responsive and shape-changing materials. Here we propose a new fast and easy approach to the nanoarchitectonics of graphene oxide nanosheets to form curved interfaces. Our technology involves computer-aid design, three dimensional (3D) printing, and casting curved templates for the assembly of two-dimensional (2D) nanosheets. We demonstrate the feasibility of our approach for the nanoarchitectonics of graphene oxide flakes, though it can be expanded to include the whole family of 2D materials. The prepared free-standing saddle-shaped graphene oxide membranes show highly ordered nanostructure, typical for flat 2D multilayered materials. We optimize the preparation conditions to construct robust two-dimensional membranes with nanostructured architecture and controllable thickness and curvature.

Emerging soft exoskeletons pose urgent needs for high-performance strain sensors with tunable linear working windows to achieve a high-precision control loop. Still, the state-of-the-art strain sensors require further advances to simultaneously satisfy multiple sensing parameters, including high sensitivity, reliable linearity, and tunable strain ranges. Besides, a wireless sensing system is highly desired to enable facile monitoring of soft exoskeleton in real time, but is rarely investigated. Herein, wireless Ti3C2Tx MXene strain sensing systems were fabricated by developing hierarchical morphologies on piezoresistive layers and incorporating regulatory resistors into circuit designs as well as integrating the sensing circuit with near-field communication (NFC) technology. The wireless MXene sensor system can simultaneously achieve an ultrahigh sensitivity (gauge factor ≥ 14,000) and reliable linearity (R2 ≈ 0.99) within multiple user-designated high-strain working windows (130% to ≥900%). Additionally, the wireless sensing system can collectively monitor the multisegment exoskeleton actuations through a single database channel, largely reducing the data processing loading. We finally integrate the wireless, battery-free MXene e-skin with various soft exoskeletons to monitor the complex actuations that assist hand/leg rehabilitation.