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Selective hydrogenation of nitrobenzene (SHN) is an important approach to synthesize aniline, an essential intermediate with extremely high research significance and value in the fields of textiles, pharmaceuticals and dyes. SHN reaction requires high temperature and high hydrogen pressure via the conventional thermal-driven catalytic process. On the contrary, photocatalysis provides an avenue to achieve high nitrobenzene conversion and high selectivity towards aniline at room temperature and low hydrogen pressure, which is in line with the sustainable development strategies. Designing efficient photocatalysts is a crucial step in SHN. Up to now, several photocatalysts have been explored for photocatalytic SHN, such as TiO2, CdS, Cu/graphene and Eosin Y. In this review, we divide the photocatalysts into three categories based on the characteristics of the light harvesting units, including semiconductors, plasmonic metal-based catalysts and dyes. The recent progress of the three categories of photocatalysts is summarized, the challenges and opportunities are pointed out and the future development prospects are described. It aims to give a clear picture to the catalysis community and stimulate more efforts in this research area.

Distributed Denial-of-Service (DDoS) attack is a long-lived attack that is hugely harmful to the Internet. In particular, the emergence of a new type of DDoS called Link Flooding Attack (LFA) makes the detection and defense more difficult. In LFA, the attacker cuts off a specific area by controlling large numbers of bots to send low-rate traffic to congest selected links. Since the attack flows are similar to the legitimate ones, traditional schemes like anomaly detection and intrusion detection are no longer applicable. Blockchain provides a new solution to address this issue. In this paper, we propose a blockchain-based LFA detection scheme, which is deployed on routers and servers in and around the area that we want to protect. Blockchain technology is used to record and share the traceroute information, which enables the hosts in the protected region to easily trace the flow paths. We implement our scheme in Ethereum and conduct simulation experiments to evaluate its performance. The results show that our scheme can achieve timely detection of LFA with a high detection rate and a low false positive rate, as well as a low overhead.

This paper considers opportunistic spectrum access (OSA) at a cognitive network. Recognizing hardware and energy constraints, we assume that a user may not be able to perform full spectrum sensing. The OSA process is usually formulated as a partially observable Markov decision process (POMDP) model with hybrid actions. In the past work, we improved this model by dividing one state transition procedure into two neighboring state transition procedures with the single action. In this study, we propose a value iteration algorithm based on the state estimation to solve the exponential complexity problem when the channels of the spectrum are relevant with each other. The algorithm uses the Markovian state estimate to replace the partially observable state, and iterates the heuristic value function of the state estimation to optimize the actions. The experiments show that the existing algorithms have better performance with the improved model, and the proposed algorithm can be used to solve the problem with relevant channels.

Particulate colloids exist in a broad range of natural, biological and industrial systems. To examine the particle aggregation behaviour in these systems, and to propose feasible aggregation control strategies, a novel computational model is developed based on the Eulerian-Lagrangian framework. Computational fluid dynamics techniques are utilised to simulate the flow field of the base fluid, and discrete phase modelling approach is used to compute the particle trajectories, with both long-range and contact particle-particle interactions considered. Two-way coupling is adopted to model particle-fluid interactions. Three studies of different colloidal systems are conducted using the developed model. In the first study, the morphology of gold nanoparticles is analysed at various particle volume fractions and base fluid pH levels. It is observed that small isolated particle clusters are formed at low particle volume fractions, whilst a more complex and interconnect particle network is formed at higher particle volume fractions. It is also found that larger but more compact particle aggregates are formed under acidic conditions (pH = 3.5), compared with neutral and basic conditions (pH = 6.7 and 9.4). The second study investigates the particle structures in magnetic nanofluids under static external magnetic fields with constant direction and magnitude. In the absence of the magnetic field, a randomly-oriented particle network is formed. With the application of external magnetic fields, distinct chain-like particle structures are observed parallel to the direction of the applied magnetic fields. The formation of the chain-like structures is more rapid, and the chain-like structures formed are thicker at higher magnitudes of the magnetic field. This observation is consistent between the two-dimensional and three-dimensional simulations. A novel particle aggregation control strategy is proposed in the third study via the application of alternating and rotating magnetic fields. It is demonstrated that the aggregation of magnetic-responsive particles is promoted under rotating magnetic fields, especially at higher rotating frequencies. It is also demonstrated that complete particle disaggregation can be achieved under alternating magnetic fields. Lastly, it is concluded that the computational model developed is a breakthrough in numerically predicting the particle aggregation behaviour in colloidal systems, and can be extended to many other studies.