Stereoselective Synthesis of Polysubstituted Conjugated Dienes Enabled by Photo-Driven Sequential Sigmatropic Rearrangement.

We here reported a highly stereoselective method for the synthesis of polysubstituted conjugated dienes from α-aryl α-diazo alkynyl ketones and pyrazole-substituted unsymmetric aminals under mild conditions, which was promoted by photo-irridation and involved with 1,6-dipolar intermediate and quadruple sigmatropic rearrangements, was successfully developed. In this transformation, the cleavage of four bonds and the recombination of five bonds were implemented in one operational step. This protocol provided a modular tool for constructing dienes from amines, pyrazoles and α-alkynyl-α-diazoketones in one-pot manner. The results of mechanistic investigation indicated that the plausible reaction path underwent the 1,6-sigmatropic rearrangement instead of the 1,5-sigmatropic rearrangement.

Transparent Self-Healing Anti-Freezing Ionogel for Monolayered Triboelectric Nanogenerator and Electromagnetic Energy-Based Touch Panel.

The advent of Internet of Things and artificial intelligence era necessitates the advancement of self-powered electronics. However, prevalent multifunctional electronics still face great challenges in rigid electrodes, stacked layers, and external power sources to restrict the development in flexible electronics. Here, a transparent, self-healing, anti-freezing (TSA) ionogel composed of fluorine-rich ionic liquid and fluorocarbon elastomer, which is engineered for monolayered triboelectric nanogenerators (M-TENG) and electromagnetic energy-based touch panels is developed. Notably, the TSA-ionogel exhibits remarkable features including outstanding transparency (90%), anti-freezing robustness (253 K), impressive stretchability (600%), and repetitive self-healing capacity. The resultant M-TENG achieves a significant output power density (200 mW m-2 ) and sustains operational stability beyond 1 year. Leveraging this remarkable performance, the M-TENG is adeptly harnessed for biomechanical energy harvesting, self-powered control interface, electroluminescent devices, and enabling wireless control over electrical appliances. Furthermore, harnessing Faraday's induction law and exploiting human body's intrinsic antenna properties, the TSA-ionogel seamlessly transforms into an autonomous multifunctional epidermal touch panel. This touch panel offers impeccable input capabilities through word inscription and participation in the Chinese game of Go. Consequently, the TSA-ionogel's innovation holds the potential to reshape the trajectory of next-generation electronics and profoundly revolutionize the paradigm of human-machine interaction.

All Resistive Pressure-Temperature Bimodal Sensing E-Skin for Object Classification.

Electronic skin (E-skin) with multimodal sensing ability demonstrates huge prospects in object classification by intelligent robots. However, realizing the object classification capability of E-skin faces severe challenges in multiple types of output signals. Herein, a hierarchical pressure-temperature bimodal sensing E-skin based on all resistive output signals is developed for accurate object classification, which consists of laser-induced graphene/silicone rubber (LIG/SR) pressure sensing layer and NiO temperature sensing layer. The highly conductive LIG is employed as pressure-sensitive material as well as the interdigital electrode. Benefiting from high conductivity of LIG, pressure perception exhibits an excellent sensitivity of -34.15 kPa-1 . Meanwhile, a high temperature coefficient of resistance of -3.84%°C-1 is obtained in the range of 24-40 °C. More importantly, based on only electrical resistance as the output signal, the bimodal sensing E-skin with negligible crosstalk can simultaneously achieve pressure and temperature perception. Furthermore, a smart glove based on this E-skin enables classifying various objects with different shapes, sizes, and surface temperatures, which achieves over 92% accuracy under assistance of deep learning. Consequently, the hierarchical pressure-temperature bimodal sensing E-skin demonstrates potential application in human-machine interfaces, intelligent robots, and smart prosthetics.

A Digital-Analog Bimodal Memristor Based on CsPbBr3 for Tactile Sensory Neuromorphic Computing.

Memristor with digital and analog bipolar bimodal resistive switching offers a promising opportunity for the information-processing component. However, it still remains a huge challenge that the memristor enables bimodal digital and analog types and fabrication of artificial sensory neural network system. Here, a proposed CsPbBr3 -based memristor demonstrates a high ON/OFF ratio (>103 ), long retention (>104 s), stable endurance (100 cycles), and multilevel resistance memory, which acts as an artificial synapse to realize fundamental biological synaptic functions and neuromorphic computing based on controllable resistance modulation. Moreover, a 5 × 5 spinosum-structured piezoresistive sensor array (sensitivity of 22.4 kPa-1 , durability of 1.5 × 104 cycles, and fast response time of 2.43 ms) is constructed as a tactile sensory receptor to transform mechanical stimuli into electrical signals, which can be further processed by the CsPbBr3 -based memristor with synaptic plasticity. More importantly, this artificial sensory neural network system combined the artificial synapse with 5 × 5 tactile sensing array based on piezoresistive sensors can recognize the handwritten patterns of different letters with high accuracy of 94.44% under assistance of supervised learning. Consequently, the digital-analog bimodal memristor would demonstrate potential application in human-machine interaction, prosthetics, and artificial intelligence.

Ultrastretchable High-Conductivity MXene-Based Organohydrogels for Human Health Monitoring and Machine-Learning-Assisted Recognition.

Conductive hydrogels as promising candidates of wearable electronics have attracted considerable interest in health monitoring, multifunctional electronic skins, and human-machine interfaces. However, to simultaneously achieve excellent electrical properties, superior stretchability, and a low detection threshold of conductive hydrogels remains an extreme challenge. Herein, an ultrastretchable high-conductivity MXene-based organohydrogel (M-OH) is developed for human health monitoring and machine-learning-assisted object recognition, which is fabricated based on a Ti3C2Tx MXene/lithium salt (LS)/poly(acrylamide) (PAM)/poly(vinyl alcohol) (PVA) hydrogel through a facile immersion strategy in a glycerol/water binary solvent. The fabricated M-OH demonstrates remarkable stretchability (2000%) and high conductivity (4.5 S/m) due to the strong interaction between MXene and the dual-network PVA/PAM hydrogel matrix and the incorporation between MXene and LS, respectively. Meanwhile, M-OH as a wearable sensor enables human health monitoring with high sensitivity and a low detection limit (12 Pa). Furthermore, based on pressure mapping image recognition technology, an 8 × 8 pixelated M-OH-based sensing array can accurately identify different objects with a high accuracy of 97.54% under the assistance of a deep learning neural network (DNN). This work demonstrates excellent comprehensive performances of the ultrastretchable high-conductive M-OH in health monitoring and object recognition, which would further explore extensive potential application prospects in personal healthcare, human-machine interfaces, and artificial intelligence.