Sensing devices are key nodes for information detection, processing, and conversion and are widely applied in different fields such as industrial production, environmental monitoring, and defense. However, increasing demand of these devices has complicated the application scenarios and diversified the detection targets thereby promoting the continuous development of sensing materials and detection methods. In recent years, Ti_n+1C_nT_x (n = 1, 2, 3) MXenes with outstanding optical, electrical, thermal, and mechanical properties have been developed as ideal candidates of sensing materials to apply in physical, chemical, and biological sensing fields. In this review, depending on optical and electrical sensing signals, we systematically summarize the application of Ti_n+1C_nT_x in nine categories of sensors such as strain, gas, and fluorescence sensors. The excellent sensing properties of Ti_n+1C_nT_x allow its further development in emerging intelligent and bionic devices, including smart flexible devices, bionic E-skin, neural network coding and learning, bionic soft robot, as well as intelligent artificial eardrum, which are all discussed briefly in this review. Finally, we present a positive outlook on the potential future challenges and perspectives of MXene-based sensors. MXenes have shown a vigorous development momentum in sensing applications and can drive the development of an increasing number of new technologies.

Random bit generators are critical for information security, cryptography, stochastic modeling, and simulations. Speed and scalability are key challenges faced by current physical random bit generation. Herein, we propose a massively parallel scheme for ultrafast random bit generation towards rates of order 100 terabit per second based on a single micro-ring resonator. A modulation-instability-driven chaotic comb in a micro-ring resonator enables the simultaneous generation of hundreds of independent and unbiased random bit streams. A proof-of-concept experiment demonstrates that using our method, random bit streams beyond 2 terabit per second can be successfully generated with only 7 comb lines. This bit rate can be easily enhanced by further increasing the number of comb lines used. Our approach provides a chip-scale solution to random bit generation for secure communication and high-performance computation, and offers superhigh speed and large scalability.

Common-signal-induced synchronization of semiconductor lasers have promising applications in physical-layer secure transmission with high speed and compatibility with the current fiber communication. Here, we propose an ultra-long-distance laser synchronization scheme by utilizing random digital optical communication signal as the common drive signal. By utilizing the long-haul optical coherent communication techniques, high-fidelity fiber transmission of the digital drive can be achieved and thus ultra-long-distance synchronization is expected. Experiments were implemented with distributed feedback lasers injected by a random-digital phase-modulated drive light. Results show that high-quality synchronization can be achieved as the drive signal rate is larger than the laser relaxation frequency and the transmission bit error ratio is below a critical value. Chaos synchronization over 8191-km fiber transmission was experimentally achieved. Compared to traditional common-signal-induced synchronization using analog drive signal such as chaos, the distance is increased by 8 times, and complicated hardware devices for channel impairment compensation are no longer required. In addition, the proposed method does not sacrifice communication capacity like traditional methods which need a channel to transmit analog drive signal. It is therefore believed that this common-digital-signal induced laser synchronization paves a way for secure backbone and submarine transmission.

The performance of high-speed intensity modulation direct detection (IM-DD) transmissions is severely degraded by the linear inter-symbol interference (ISI), due to both bandwidth-constrained optoelectrical components and the chromatic dispersion (CD). Meanwhile, nonlinear ISI due to intensity modulation and square-law detection can further deteriorate the IM-DD system performance significantly. Here, we first propose three metrics to quantify those impairments in IM-DD transmissions. Then, we compare both the BER performance and computational complexity of different DSP schemes to enable short-reach high-speed IM-DD transmissions with severe bandwidth constraint, using a C-band PAM-4 experimental test-bed with an end-to-end 10-dB bandwidth of 17 GHz. Those DSP schemes include receiver-side feedforward equalization (FFE), receiver-side Volterra filter equalization (VFE), transmitter-side Tomlinson-Harashima precoding (THP) together with receiver-side FFE, transmitter-side THP together with receiver-side VFE, channel-shortening filter (CSF) together with maximum likelihood sequence estimation implemented with linear branch metrics (MLSE-LI-BM), and CSF together with MLSE implemented with nonlinear branch metrics (MLSE-NL-BM). Our experimental results indicate that, only the combination of CSF and MLSE-NL-BM enables a 112 Gbit/s transmission over 5 km fiber at the KP4-FEC threshold, but with an increased computational complexity.

Providing physical layer security at the lowest network layer in fiber-optic communication systems is a technical challenge worldwide. Here, we propose and experimentally demonstrate a pure hardware optical encryption scheme based on temporal spreading and self-feedback phase encryption for high-speed and long-distance physical-layer secure optical communication. A record high bit-rate–distance product of 6400 Gb/s km is successfully achieved by the secure transmission of a 32 Gb/s on-off-keying modulated confidential signal over a 200 km optical fiber link. The demonstrated scheme is fully compatible with conventional optical transmission systems and can be operated in a pluggable manner, which may pave a new path to ultra-high-speed physical-layer secure optical communication in the future.

Supervised learning, a popular tool in modern science and technology, thrives on huge amounts of labeled data. Physics-enhanced deep neural networks offer an effective solution to alleviate the data burden by incorporating an analytical model that interprets the underlying physical processes. However, it completely fails in tackling systems without analytical solution, where wave scattering systems with multiple input multiple output are typical examples. Herein, we propose a concept of deep empirical neural network (DENN) that is a hybridization of a deep neural network and an empirical model, which enables seeing through an opaque scattering medium in an untrained manner. The DENN does not rely on labeled data, all while delivering as high as 58% improvement in fidelity compared with the supervised learning using 30000 data pairs for achieving the same goal of optical phase retrieval. The DENN might shed new light on the applications of deep learning in physics, information science, biology, chemistry and beyond.