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Abstract Recently reported direct growth of highly crystalline centimetre-sized black phosphorus (BP) thin films on mica substrates by pulsed laser deposition (PLD) has attracted considerable research interest. However, an effective and general transfer method to incorporate them into (opto-)electronic devices is still missing. Here, we show a wet transfer method utilizing ethylene-vinyl acetate (EVA) and an ethylene glycol (EG) solution to transfer high-crystalline large-area PLD-BP films onto SiO2/Si substrates. The transferred films were used to fabricate BP-based bottom-gate field-effect transistor (FET) arrays exhibiting good uniformity and continuity, with carrier mobility and current switching ratios comparable to those obtained in as-grown BP films on mica substrates. Our work presents a promising transfer strategy for scalable integration of on-substrate grown 2D BP into devices with more complex structures and further investigation of material properties.

With the development of artificial intelligence (AI), deep learning is widely used in various industries. At CRYPTO 2019, researchers used deep learning to analyze the block cipher for the first time and constructed a differential neural network distinguisher to meet a certain accuracy. In this paper, a mixture differential neural network distinguisher using ResNet is proposed to further improve the accuracy by exploring the mixture differential properties. Experiments are conducted on SIMON32/64, and the accuracy of the 8-round mixture differential neural network distinguisher is improved from 74.7% to 92.3%, compared with that of the previous differential neural network distinguisher. The prediction accuracy of the differential neural network distinguisher is susceptible to the choice of the specified input differentials, whereas the mixture differential neural network distinguisher is less affected by the input difference and has greater robustness. Furthermore, by combining the probabilistic expansion of rounds and the neutral bit, the obtained mixture differential neural network distinguisher is extended to 11 rounds, which can realize the 12-round actual key recovery attack on SIMON32/64. With an appropriate increase in the time complexity and data complexity, the key recovery accuracy of the mixture differential neural network distinguisher can be improved to 55% as compared to 52% of the differential neural network distinguisher. The mixture differential neural network distinguisher proposed in this paper can also be applied to other lightweight block ciphers.

Abstract Ferroelectric materials with switchable electrical polarization have been widely used in tunnel junctions, non‐volatile memories, and field‐effect transistors. Large‐area organic ferroelectric polymers compatible with silicon or flexible substrates have played a crucial role in nanoelectronics. Poly(vinylidene fluoride‐trifluoroethylene) P(VDF‐TrFE) as a representative, different from traditional bulk oxide ferroelectrics in terms of atom arrangements and fabrication methods, has frequently been used as the ferroelectric gate for high‐performance electronic, optical, and synaptic transistors. Ferroelectric copolymers have gradually become a promising and versatile alternative for inorganic ferroelectrics. This review will focus on the interface engineering and device applications of 2D materials/ferroelectric P(VDF‐TrFE) hybrid structures. The intrinsic ferroelectric properties and unique features of P(VDF‐TrFE) are first elucidated. Next, typical device structures with ferroelectric gating effect followed by its physical working mechanisms will be discussed. In the next section, diverse nanoelectronics applications of ferroelectric field effect transistors based on P(VDF‐TrFE), including optoelectronic devices, non‐volatile memories, neuromorphic computing, and negative capacitance transistors, are clarified. Moreover, existing challenges and further development for ferroelectric polymer will be discussed. With an emphasis on the ferroelectric polymer gate and related issues, this review provides a timely summary of current physical understanding and progresses.

Summary: Background: Valproic acid (VPA) represents one of the most efficient antiseizure medications (ASMs) for both general and focal seizures, but some patients may have inadequate control by VPA monotherapy. In this study, we aimed to verify the hypothesis that excitatory dynamic rebound induced by inhibitory power may contribute to the ineffectiveness of VPA therapy and become a predictor of post-operative inadequate control of seizures. Methods: Awake craniotomy surgeries were performed in 16 patients with intro-operative high-density electrocorticogram (ECoG) recording. The relationship between seizure control and the excitatory rebound was further determined by diagnostic test and univariate analysis. Thereafter, kanic acid (KA)-induced epileptic mouse model was used to confirm that its behavior and neural activity would be controlled by VPA. Finally, a computational simulation model was established to verify the hypothesis. Findings: Inadequate control of seizures by VPA monotherapy and post-operative status epilepticus are closely related to a significant excitatory rebound after VPA injection (rebound electrodes≧5/64, p = 0.008), together with increased synchronization of the local field potential (LFP). In addition, the neural activity in the model mice showed a significant rebound on spike firing (53/77 units, 68.83%). The LFP increased the power spectral density in multiple wavebands after VPA injection in animal experiments (p

Invasive brain-computer interfaces (BCI) have made great progress in the reconstruction of fine hand movement parameters for paralyzed patients, where superficial measurement modalities including electrocorticography (ECoG) and micro-array recordings are mostly used. However, these recording techniques typically focus on the signals from the sensorimotor cortex, leaving subcortical regions and other cortical regions related to the movements largely unexplored. As an intracranial recording technique for the presurgical assessments of brain surgery, stereo-encephalography (SEEG) inserts depth electrodes containing multiple contacts into the brain and thus provides the unique opportunity for investigating movement-related neural representation throughout the brain. Although SEEG samples neural signals with high spatial-temporal resolutions, its potential of being used to build BCIs has just been realized recently, and the decoding of SEEG activity related to hand movements has not been comprehensively investigated yet. Here, we systematically evaluated the factors influencing the performance of movement decoding using SEEG signals recorded from 32 human subjects performing a visually-cued hand movement task. Our results suggest that multiple regions in both lateral and depth directions present significant neural selectivity to the task, whereas the sensorimotor area, including both precentral and postcentral cortex, carries the richest discriminative neural information for the decoding. The posterior parietal and prefrontal cortex contribute gradually less, but still rich sources for extracting movement parameters. The insula, temporal and occipital cortex also contains useful task-related information for decoding. Under the cortex layer, white matter presents decodable neural patterns but yields a lower accuracy (42.0 ± 0.8%) than the cortex on average (44.2 ± 0.8%, p

Background: Lives of older adults have been greatly affected by the COVID-19 pandemic.Methods: A telephone survey was conducted among the older adults aged 60 and above who lived in downtown Shanghai. We compared the lifestyle, mood, and disease management of older adults before and during the COVID-19 pandemic.Results: One hundred and fifty-six older adults in Shanghai completed the survey. The proportions of older adults with adequate consumption of meat (49.4% vs. 53.1%, P = 0.0339) and eggs (73.7% vs. 77.6%, P = 0.0143) were significantly higher than before. Participants spent significantly more time on housework (median: 2.0, IQR:1.0–3.0 vs. median: 2.0, IQR:1.0–2.0 h/day; P = 0.0361) and leisure activities (median: 7.0, IQR: 5.0–8.6 vs. median: 6.0, IQR: 4.0–8.5 h/day; P

As a minimally invasive recording technique, stereo-electroencephalography (SEEG) measures intracranial signals directly by inserting depth electrodes shafts into the human brain, and thus can capture neural activities in both cortical layers and subcortical structures. Despite gradually increasing SEEG-based brain-computer interface (BCI) studies, the features utilized were usually confined to the amplitude of the event-related potential (ERP) or band power, and the decoding capabilities of other time-frequency and time-domain features have not been demonstrated for SEEG recordings yet. In this study, we aimed to verify the validity of time-domain and time-frequency features of SEEG, where classification performances served as evaluating indicators. To do this, using SEEG signals under intermittent auditory stimuli, we extracted features including the average amplitude, root mean square, slope of linear regression, and line-length from the ERP trace and three traces of band power activities (high-gamma, beta, and alpha). These features were used to detect the active state (including activations to two types of names) against the idle state. Results suggested that valid time-domain and time-frequency features distributed across multiple regions, including the temporal lobe, parietal lobe, and deeper structures such as the insula. Among all feature types, the average amplitude, root mean square, and line-length extracted from high-gamma (60–140 Hz) power and the line-length extracted from ERP were the most informative. Using a hidden Markov model (HMM), we could precisely detect the onset and the end of the active state with a sensitivity of 95.7 ± 1.3% and a precision of 91.7 ± 1.6%. The valid features derived from high-gamma power and ERP in this work provided new insights into the feature selection procedure for further SEEG-based BCI applications.

Motor imagery-based brain-computer interfaces (BCIs) have been studied without controlling subjects’ gaze fixation position previously. The effect of gaze fixation and covert attention on the behavioral performance of BCI is still unknown. This study designed a gaze fixation controlled experiment. Subjects were required to conduct a secondary task of gaze fixation when performing the primary task of motor imagination. Subjects’ performance was analyzed according to the relationship between motor imagery target and the gaze fixation position, resulting in three BCI control conditions, i.e., congruent, incongruent, and center cross trials. A group of fourteen subjects was recruited. The average group performances of three different conditions did not show statistically significant differences in terms of BCI control accuracy, feedback duration, and trajectory length. Further analysis of gaze shift response time revealed a significantly shorter response time for congruent trials compared to incongruent trials. Meanwhile, the parietal occipital cortex also showed active neural activities for congruent and incongruent trials, and this was revealed by a contrast analysis of R-square values and lateralization index. However, the lateralization index computed from the parietal and occipital areas was not correlated with the BCI behavioral performance. Subjects’ BCI behavioral performance was not affected by the position of gaze fixation and covert attention. This indicated that motor imagery-based BCI could be used freely in robotic arm control without sacrificing performance.