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Development of a functional sorbent for effective crude oil absorption is essential to address large-scale spilling incidents. Herein, we demonstrate a facile method for preparing a superhydrophobic and photothermal PDMS/CuS/PDA@MF sponge through sequential depositions of PDA, CuS nanoparticles, and a PDMS layer onto a melamine sponge. The optimized composite sponge exhibits a superhydrophobic surface property, high absorption capacity for oils, robust recycling, and excellent photothermal conversion performance. Under sunlight irradiation, the sponge can be rapidly heat up for effectively reducing the viscosity of the surrounding crude oil in order to enhance its fluidity. As a result, uptake of crude oil can be achieved continuously at approximately 5.3 g/min using a peristaltic pump. Overall, we believe that a simple fabrication method from low-cost reagents and excellent crude oil remediation performance render the PDMS/CuS/PDA@MF sponge as an excellent sorbent candidate for remediating crude oil spill.

A generalizable approach for improving the stability of polylactide-based (PLA-based) micelles for encapsulating nanoparticles (NPs) is demonstrated, using stereocomplexation between a pair of poly (ethylene glycol)-b-poly(d-lactide)/poly(ethylene glycol)-b-poly(l-lactide) block copolymer blends. Three different superparamagnetic ferrite-based NPs with distinct nanostructures are first prepared by the high-temperature pyrolysis method, including spherical MnFe2O4, cubic MnFe2O4, and core–shell MnFe2O4@Fe3O4. The diameters of these NPs are approximately 7–10 nm as revealed by transmission electron microscopy. These hydrophobic NPs can be encapsulated within self-assembled, stereocomplexed PLA (sc-PLA) micelles. All sc-PLA micelle systems loaded with three different NPs exhibit enhanced stability at elevated temperatures (20–60 °C) and with extended storage time (∼96 h) compared with analogous samples without stereocomplex formation, confirmed by dynamic light scattering measurements. The magnetic NP-loaded micelles with mean diameters of approximately 150 nm show both biocompatibility and superparamagnetic property. Under a 1.5 T magnetic field, cubic MnFe2O4(c-MnFe2O4)-loaded micelles exhibit an excellent negative contrast enhancement of MR signals (373 mM–1·s–1), while core–shell MnFe2O4@Fe3O4-loaded micelles show a slightly lower signal for MR imaging (275 mM–1·s–1). These results suggest the potential of using sc-PLA-based polymer micelles as universal carriers for magnetic resonance imaging contrast agents with improved stability for different applications such as cancer diagnosis.

In networked control systems (NCS), a certain class of attacks on the communication network is known to raise traffic flows causing delays and packet losses to increase. This paper presents a novel neural network (NN)-based attack detection and estimation scheme that captures the abnormal traffic flow due to a class of attacks on the communication links within the feedback loop of an NCS. By modeling the unknown network flow as a nonlinear function at the bottleneck node and using a NN observer, the network attack detection residual is defined and utilized to determine the onset of an attack in the communication network when the residual exceeds a predefined threshold. Upon detection, another NN is used to estimate the flow injected by the attack. For the physical system, we develop an attack detection scheme by using an adaptive dynamic programming-based optimal event-triggered NN controller in the presence of network delays and packet losses. Attacks on the network as well as on the sensors of the physical system can be detected and estimated with the proposed scheme. The simulation results confirm theoretical conclusions.

A novel learning-based attack detection and estimation scheme is proposed for linear networked control systems &#x0028 NCS &#x0029 , wherein the attacks on the communication network in the feedback loop are expected to increase network induced delays and packet losses, thus changing the physical system dynamics. First, the network traffic flow is modeled as a linear system with uncertain state matrix and an optimal Q-learning based control scheme over finite-horizon is utilized to stabilize the flow. Next, an adaptive observer is proposed to generate the detection residual, which is subsequently used to determine the onset of an attack when it exceeds a predefined threshold, followed by an estimation scheme for the signal injected by the attacker. A stochastic linear system after incorporating network-induced random delays and packet losses is considered as the uncertain physical system dynamics. The attack detection scheme at the physical system uses the magnitude of the state vector to detect attacks both on the sensor and the actuator. The maximum tolerable delay that the physical system can tolerate due to networked induced delays and packet losses is also derived. Simulations have been performed to demonstrate the effectiveness of the proposed schemes.

As a necessary step, removing liquid in the wellbore plays an important role during the production of gas wells. Plunger lift is a widely-used intermittent deliquification process for gas wells. However, the manual control way and wire logging are still utilized as a downhole monitoring way for plunger lift, which is not efficient in terms of interrupting the production. This paper presents an improved solution that logging instruments canister are deployed and retrieved by means of a new assembly. With the reciprocating plunger, logging instruments canister can be carried and deployed to the bottom of a gas well to carry out logging and sampling tasks on the production demand of a field. After the deployment and logging tasks are performed, logging instruments canister is carried back to the surface by the plunger and then data is transferred to the wellhead device near field wireless communication technology. This newly developed plunger lift system comprises plunger body, deployment sub-assembly, retrieve sub-assembly and logging instruments canister. The surface device comprises RF antenna, reader and writer. Based upon the method of deployment-retrieve integration, the new deliquification process is introduced and on-line monitoring of production dynamics can be performed including P/T measurement, downhole fluid sampling, pressure build-up, etc. without interrupting production. The general solution and engineering design parameters have been confirmed by research teams, while system prototype manufacture and workbench tests are being performed. The cost-effective way combining deliquification with dynamic monitoring is developed and contributes to increasing production and the stable productivity of gas wells. It is very significant for low-pressure and low-production gas fields to achieve automation production and management.

With the increasing connectivity among computational cyber-connected elements and physical entities, a unified representation that captures the interrelationship between the cyber and the physical systems becomes increasingly important. In this paper, we propose a novel representation for developing cybersecurity schemes for physical systems wherein the cyber system states affect the physical system and vice versa. Subsequently by using this representation, an optimal strategy via Q-learning is derived for the cyber defense in the presence of an attack. Since the cyber system under attack will affect the physical system stability and performance, an optimal controller by using Q-learning is considered for the physical system with uncertain dynamics. As an example, cyber-attacks that increase the network delay and packet losses are considered and the goal of the proposed cyber defense and optimal controller is to thwart the attack and mitigate the performance degradation of the physical system due to increased delays and packet losses. An illustrative example is given where the proposed theory is evaluated on the yaw-channel control of an unmanned aerial vehicle. Simulation results show that on the cyber side, both the attacker and the defender gains their greatest payoff whereas on the physical system side, the optimal controller is able to maintain the linear system in a stable manner when the cyber state vector meets a certain desired criterion.