Boosting flexible electronics with integration of two‐dimensional materials

This dataset includes original TIFF and PNG data from original research within the project EBEAM. Precisely, there are 15 final, complex Figures, two Schemes, and seven Tables, described below: Figure 1. Machine learning‐assisted temperature-pressure electronic skin with decoupling capability (TPD e skin) enables object recognition. (A) Principle of using machine learning to recognize objects via e‐skin. (B) Structure of a one‐dimensional convolutional neural network for TPD object recognition. (C) Breakthrough in grasping objects made from 15 different materials by prosthetics. (D) Temperature-pressure frequency waveforms generated by prosthetic grasping of 15 different materials, realized by neuromorphic coding. (E) Visualization of 15 samples of signals of different frequencies by t‐distributed stochastic neighbor embedding (t‐SNE). (F) Confusion matrices for 15 types of object recognition. (G) Cognitive outcome waveform during expiration. (H) Identification and waveform of grasping thermoplastic bottles Figure 2. Application scenarios for piezoresistive sensors based on polyetherimide (PET)/MXene designs. (A) Wireless transmission system for MXene‐based sensor signals. A Bluetooth module is used for signal transmission in response to pressure on the sensor. (B) Use of MXene‐based sensor to detect the pressure of different chess pieces and thus locate them. (C) Pressure detection during the swing of a robotic arm. (D) Utilizing the brightness of an LED to reflect changes in pressure applied to the sensor. (E) Application of the MXene‐based sensor to the skin for Joule heating experiments. (F) Temperature distribution of MXene‐based sensors at different voltages. (G) Infrared thermal imaging of the MXene‐based sensor at increasing voltage, corresponding to the test results in (F) Figure 3. Arrayed flexible graphene thermal patches for patient skin temperature and hyperthermia monitoring. (A) Illustration of the process of detecting and sensing skin temperature using graphene patches. Human skin temperature perception and auxiliary heating are achieved by integrating array‐based sensor patches into medical patches. The measured temperature signal is then processed through a readout circuit and transmitted to the phone via Bluetooth to detect the skin temperature signal in real time. Additionally, the heating temperature of graphene patches can be controlled through mobile phones. (B) Photos of a graphene capacitive sensor arranged in an 8 × 8 array composition. (C) Interface structure of the sensor array utilizing graphene as an electrode. (D) Partial enlarged view of a graphene sensor. (E) Enlarged image of the flexible substrate functional area of the graphene patch in (B). Active areas include a readout front end, an analog‐to‐digital converter, a microcontroller, an Xtal XO, Bluetooth low energy (BLE), a DC/DC converter, and a battery. (F) Framework diagram of the wireless measurement and heating system for graphene patches. The design allows precise temperature measurement by alternating between measurement and heating states Figure 4. Electronic skin, artificial retina, and electronic nose designed with machine learning algorithms. (A) Deep learning based on PdSe2 piezoresistive sensors for pulse recognition and temperature readout. Deep learning steps for converting resistance to temperatures with the input of three distinct pulse signals. The detection of pulse temperature is achieved by employing the pressure signals of pulse beats. (B) Temperature–pulse curves representing distinct pulse shapes. (C) Stabilization of training and validation losses at low values after 500 training cycles. (D) Temperature readout through deep learning with 98% accuracy. (E) MoSSe‐based artificial retina with integrated sensing, storage, and computing functions. (F) Graphene‐based artificial nose identifying four different volatile organic molecules Figure 5. (A–C) MoS2‐based field‐effect transistor employed for information encryption. (D) Image captured by the MoS2 image sensor. (E) Transfer characteristics of the MoS2 transistor, determining binary values based on the intercept of the linear fitting. (F) Histogram of the gate‐voltage intercept, with blue denoting 0 and red denoting 1; reference gate voltage is −1 V. (G) MoS2 transistor leakage current curve in the on/off state. (H) Histogram of drain current for binary data: 0 when ID lower 18 μA and 1 when ID higher 18 μA. (I) PUF pattern. (J) Sequential steps of image encryption and decryption: the image, captured by the sensor, is encrypted with a PUF key and is subsequently decrypted with the corresponding key Figure 6. Optical non‐contact control system based on a PtTex-Si sensor array for human–machine interaction. (A) Scheme of a photomultiplier transistor. (B) Optical image of the sensor array. (C) Flowchart depicting the process of shadow encoding and recognition. Converting photocurrent into a discrete signal enables instruction retrieval. (D) Process of encoding a photocurrent signal using the gradient approach. (E) Output displaying the encoding of shadows Figure 7. NbS2-MoS2‐based neurally inspired optical sensor array for high‐precision dynamic image recognition and single‐point motion trajectory extraction. (A) Schematic diagram of the structure of the NbS2-MoS2‐based vision system, which consists of a 100‐pixel NbS2-MoS2 optical sensing array. (B) Scheme and circuit diagram of the NbS2-MoS2 optical sensing array. (C) Optical micrograph of a 100‐pixel sensor array and scheme of a NbS2-MoS2 phototransistor Figure 8. Supercapacitor woven bracelet based on the MXene coaxial structure for charging a watch. (A) CV curves of a single zinc‐ion hybrid fiber supercapacitor (FSC) and two supercapacitors connected in series and parallel, respectively. (B) Plots of capacitance and energy of the supercapacitors versus length. (C) Bracelet weaving by consuming a 1.5 m coaxial FSC. The bracelet provides electric power for a watch and LEDs in a glove Figure 9. Three‐dimensional motion detection using an MXene‐based TENG. (A) Scheme of a 4 x 4 TENG sensor array. The motion trajectory of a finger positioned above can be captured using the sensor array. (B) Perception of finger linear motion above the sensor. (C) Capture of the finger motion trajectory when moving in a curved path. (D) Blind navigation by installing the sensor on a walking cane. (E) Photograph of the non‐contact sensor. (F) Voltage signal output when the finger moves above the six planes (A–F) Figure 10. Graphene human–robot interfaces empowered by machine learning based on graphene acoustic transducers. (A) Auditory and vocal capabilities of the robot system empowered by the system. After training using convolutional neural networks, it can recognize different identities and emotional characteristics, allowing intelligent communication and responses. (B) Schematic representation of the graphene-PI-graphene structure formed through laser irradiation. (C) SEM image of graphene. (D) Human–robot interfaces attached to a robot. (E and F) TENG operating in microphone mode, detecting sound vibrations through surface‐charge changes. (G) TENG in loudspeaker mode, generating acoustic waves through the thermoacoustic effect Figure 11. Spiking neural network (SNN) structure based on a hybrid 2D‐CMOS microchip. (A) SNN structure. The image from the Modified National Institute of Standards and Technology (MNIST) database is edited into a column vector with 784 input neurons. Pixel intensity is encoded by the firing pattern of the input neurons. Unsupervised training of neurons connecting the input and excitation layers results in labeled trained neurons. These, together with firing patterns, are transmitted to the decision block for feedback, allowing inference of the presented images. (B) Synaptic connection evolution training conducted on 400 excitatory and 400 inhibitory layer neurons. (C) Obfuscation matrix, which provides a visual representation of dataset accuracy. (D) 50 Monte Carlo simulations of 400 excitatory and 400 inhibitory layer neurons of SNN. After 50 iterations, the system accuracy reached 90percent. (E) Schematic diagram of the neuron–synapse–neuron module circuit design based on h‐BN. (F) SPICE‐like simulation of synaptic signals from one‐transistor‐one‐memristor cells. (G) Neuronal membrane potential simulated by SPICE simulation Figure 12. Comparison between the traditional cross‐computing structure and the cyclic logic computing scheme. (A) Cross‐operation structure. The input and output memristors are in the same row and column. (B) Circular logic computational structure. The state and inverted state of the cell computer are correlated with the resistance state of the two circuits of the memristor, and the state of its neighboring cells determines the state of each cell through a cyclic logic calculation scheme. (C) Schematic diagram of the design of the memristor array to implement the cyclic logic operation scheme. (D) Design diagram of the cyclic logic calculation scheme. The mapping scheme divides the input signal into two modes-calculation and writing. The state calculation of the cellular automaton is realized through the calculation mode, whereas the cell automaton storage mode is realized through the write mode Figure 13. In‐memory computing design for a hybrid logic circuit with a MoS2‐based transistor and memristor. (A and B) I–V curves for memristors and MoS2‐based transistors, respectively. (C and D) Simple NAND and AND logic operation verification using a memristor hybrid circuit. (E and F) Measurements of both NAND and AND logic operations. (G and H) Measurement of voltage deviation by 100 simulations using Vdd and VR Figure 14. Demonstration of circular logic solutions for 1D cellular and basic cellular automata. (A) Optical image of a 1D cell automaton. (B) Circuit diagram of a 1D cell automaton. Green dotted coil indicates a basic unit. Each basic unit consists of a memory resistor and an auxiliary memory resistor. CAx and CAx represent the resistance value and resistance inverse value of the cellular automaton respectively. (C) 110 logical operation of ECA rules that describe the corresponding operation in a circular logic operation. (D) Time series in which the 110 operation triggers the signal. (E) Evolution of memory resistor states under different conversion rules Figure 15. Schematic diagram of the structure of a memristor textile network with a Ag-MoS2-HfAlOx-CNT heterostructure. (A) E‐textile memristor network with remodeled synapses at the upper layer and neuromorphic functions at the lower layer. (B) Unit device structure of the Ag-MoS2-HfAlOx-CNT heterostructure. (C) SEM images of the Ag-MoS2-HfAlOx-CNT heterostructure. (D) Artificial synaptic function simulation using the reconfigurable memristor Scheme 1. Typical applications of 2D material‐empowered flexible and wearable electronics. (1) Flexible and wearable electronics. (A) Pulse temperature measurement. (B) Rechargeable gloves. (2) Flexible energy storage and conversion. (C) E‐fabric for charging. (D) Uses of mechanical and thermal energy to create sound Scheme 2. Current situation and future development trend of flexible electronics Table 1. Roles of 2D materials in flexible electronics Table 2. Two-dimensional material-based flexible sensors according to their sensing from different physical signals Table 3. Four types of 2D material-based tactile sensors Table 4. Roles of 2D materials in bioelectronic devices and their features Table 5. Two-dimensional material-based flexible solid-state supercapacitors Table 6. Flexible solid-state lithium batteries from two-dimensional materials Table 7. Comparison of the performance of TENGs before and after the incorporation of 2D materials Funding: National Key Research and Development Program (No. 2022YFE0124200), National Natural Science Foundation of China (No. U2241221); Natural Science Foundation of Shandong Province for Excellent Young Scholars (YQ2022041), and the fund (No. SKT2203) from the State Key Laboratories of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences for support; Major Scientific and Technological Innovation Project of Shandong Province (2021CXGC010603), NSFC (No. 52022037) and Taishan Scholars Project Special Funds (TSQN201812083); Foundation (No. GZKF202107) of State Key Laboratory of Biobased Material and Green Papermaking, Qilu University of Technology, Shandong Academy of Sciences; NSFC (No. 52071225), the National Science Center and the Czech Republic under the European Regional Development Fund (ERDF) “Institute of Environmental Technology—Excellent Research” (No. CZ.02.1.01/0.0/0.0/16_019/0000853); Sino-German Center for Research Promotion (SGC) for support (No. GZ 1400), European Union’s Horizon Europe Research and Innovation Program under grant agreement No.101087143 (Electron Beam Emergent Additive Manufacturing (EBEAM)).