3D bioprinting technology and equipment based on microvalve control.

3D bioprinting technology is widely used in biomedical fields such as tissue regeneration and constructing pathological model. The prevailing printing technique is extrusion-based bioprinting. In this printing method, the bioink needs to meet both printability and functionality, which are often conflicting requirements. Therefore, this study has developed an innovative microvalve-based equipment, incorporating components such as pressure control, a three-dimensional motion platform, and microvalve. Here, we present a droplet-based method for constructing complex three-dimensional structures. By leveraging the rapid switching characteristics of the microvalve, this equipment can achieve precise printing of bio-materials with viscosities as low as 10mPa·s, significantly expanding the biofabrication window for bioinks. This technology is of great significance for 3D bioprinting in tissue engineering and lays a solid foundation for the construction of complex artificial organ tissues.

Real-time all-optical random numbers based on optical Boolean chaos.

In this work, a method of generating all-optical random numbers based on optical Boolean chaotic entropy source is proposed. This all-optical random number generation system consists of a Boolean chaotic entropy source and an optical D flip-flop. The Boolean chaotic entropy source is composed of an optical XOR gate and two self-delayed feedback; meanwhile, the optical D flip-flop is composed of two optical AND gates and one SR latch. The optical Boolean chaotic signal possesses the dynamic characteristics of complexity and binarization, so random numbers would be generated only by extracted from chaotic signals with the optical D flip-flop. This all-optical random number generation system achieves the result of 5 Gb/s random numbers that is testable. The whole process of random number generation could be completed in the optical domain without photoelectric conversion, more importantly, the device could be integrated.

Optical Boolean chaos.

Boolean chaos is widely used in physical systems for its digital-like behavior and complex dynamics. However, electronic logic devices limit the bandwidth of Boolean chaos and its development. Based on an autonomous optical Boolean network, a method of generating optical Boolean chaos with 14 GHz bandwidth is proposed, exploring the physical mechanism of the chaos generated by the system and analyzing the influences of external parameters on the dynamic characteristics of the system. The output status is mainly affected by the detection optical power, carrier recovery time of the semiconductor optical amplifier, and difference between the two self-feedback time delays.

Deep Learning-Based Security Verification for a Random Number Generator Using White Chaos.

In this paper, a deep learning (DL)-based predictive analysis is proposed to analyze the security of a non-deterministic random number generator (NRNG) using white chaos. In particular, the temporal pattern attention (TPA)-based DL model is employed to learn and analyze the data from both stages of the NRNG: the output data of a chaotic external-cavity semiconductor laser (ECL) and the final output data of the NRNG. For the ECL stage, the results show that the model successfully detects inherent correlations caused by the time-delay signature. After optical heterodyning of two chaotic ECLs and minimal post-processing are introduced, the model detects no patterns among corresponding data. It demonstrates that the NRNG has the strong resistance against the predictive model. Prior to these works, the powerful predictive capability of the model is investigated and demonstrated by applying it to a random number generator (RNG) using linear congruential algorithm. Our research shows that the DL-based predictive model is expected to provide an efficient supplement for evaluating the security and quality of RNGs.

Real-time online photonic random number generation.

We present a real-time scheme for ultrafast random number (RN) extraction from a broadband photonic entropy source. Ultralow jitter mode-locked pulses are used to sample the stochastic intensity fluctuations of the entropy source in the optical domain. A discrete self-delay comparison technology is exploited to quantize the sampled pulses into continuous RN streams directly. This scheme is bias free, eliminates the electronic jitter bottleneck confronted by currently available physical RN generators, and has no need for threshold tuning and post-processing. To demonstrate its feasibility, we perform a proof-of-principle experiment using an optically injected chaotic laser diode. RN streams at up to 7 Gb/s with verified randomness were thereby successfully extracted in real time. With the provision of a photonic entropy source with sufficient bandwidth, the present approach is expected to provide RN generation rates of several tens of gigabits per second.

A Silicon-Based ROTE Sensor for High-Q and Label-Free Carcinoembryonic Antigen Detection.

This paper presents a biosensor based on the resonant optical tunneling effect (ROTE) for detecting a carcinoembryonic antigen (CEA). In this design, sensing is accomplished through the interaction of the evanescent wave with the CEA immobilized on the sensor's surface. When CEA binds to the anti-CEA, it alters the effective refractive index (RI) on the sensor's surface, leading to shifts in wavelength. This shift can be identified through the cascade coupling of the FP cavity and ROTE cavity in the same mode. Experimental results further show that the shift in resonance wavelength increases with the concentration of CEA. The biosensor responded linearly to CEA concentrations ranging from 1 to 5 ng/mL with a limit of detection (LOD) of 0.5 ng/mL and a total Q factor of 9500. This research introduces a new avenue for identifying biomolecules and cancer biomarkers, which are crucial for early cancer detection.