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Upon the emergence of electric vehicles, accurate and non-destructive monitoring of battery electrode materials during operation is highly desirable. Structural degradation of widely adopted intercalation-based materials constitutes the origin of their capacity fading and safety deterioration. Here, we introduce entropymetry to monitor the structural changes of LiCoO2 (LCO) and its nickel (Ni)-doped derivatives at different states of charge (SOC). While simple lithium (Li) extraction on charging gives a monotonic decline of entropy change (ΔS) based on progressive vacancy occupation over Li sites, the presence of a monoclinic intermediate phase inverses the slope of the ΔS profile to reflect its limited atomic configurations with high ordering. Furthermore, Ni-doping lessens the ordering of the monoclinic phase, decreasing the height amplitude of ΔS profile in the monoclinic regime. The increased disorder by Ni-doping enhances the stability of the lattice framework, extending the cycle life with high voltage cut-off (4.6 V vs. Li/Li+). The present study highlights entropymetry as a unique, non-destructive tool in monitoring the structural ordering and relevant degradation of electrode materials in lithium-ion batteries.

The reverse osmosis (RO) technology is currently the leading desalination method. However, until recently, application of RO technology on a large scale has been primarily limited by membrane fouling. The mechanism of fouling is complex, which is not well understood in full-scale plants. Although many studies about modeling and prediction of fouling have been done, in most cases, the experimental data set of lab or pilot scale systems, which may not show fouling characteristics well in full-scale systems were used. In this study, both artificial neural network (ANN) model and tree model (TM) was evaluated to analyze long-term performance of full scale reverse osmosis desalination plant. The results of application of the ANN and TM indicated high correlation coefficients between the measured and simulated output variables. However, it is not easy to use ANN for the full scale plant operation because the final model is not expressed as a form of mathematical functions. TM has advantages over ANN because the model can be obtained as forms of simple function and it showed reasonably high R2. Therefore, TM is shown to be more adequate than ANN for developing models in which the full-scale RO plant data is considered as an input.

An electron-beam was irradiated on textile-grade polyacrylonitrile (PAN) fibers at various electron doses (200, 500, 1000, and 1500 kGy) prior to the thermal stabilization to fabricate carbon fibers (CFs). Textile-grade PAN fibers experienced superficial fusion between filaments during the thermal stabilization due to a large amount of co-monomer contents. However, electron-beam irradiation prior to thermal stabilization prevented the superficial fusion. Furthermore, the total stabilization time required to achieve the same degree of stabilization was effectively reduced as 64% by electron-beam irradiation. Various radicals were formed within textile-grade PAN fibers and they were recombined to crosslink with each other by gradually increasing the temperature. As a result, the elongation of irradiated fibers was suppressed and the glass transition temperature shifted to higher temperatures compared with the pristine fiber by increasing the electron dose. On the other hand, the onset and peak temperature of cyclization reaction for irradiated fibers were shifted to lower temperatures which indicated that the cyclization reaction was initiated at lower temperatures than pristine fibers. The tensile strength, Young's modulus, and strain-to-failure of resulting CFs were 1.83 ± 0.23 GPa, 147.44 ± 4.55 GPa, and 1.30 ± 0.15%, respectively.

In this paper, we present a highly sensitive biosensor for thrombin detection using emission light guidance by 3-dimensional (3D) photonic (PC) crystals. The emission light guidance is induced by optical resonances at the specific wavelength that can be engineered by modifying the pore size of the nanoporous PC structures, and it results in enhancing the fluorescent signals from quantum dots (Qdot)-aptamer beacons. For easy to use, we introduce a fluorescence based ‘On-Off-On’ detection protocol: first, the immobilization of Qdot-aptamer beacons on the surface of PC turns biosensor to ‘On’ state with an original fluorescent intensity; then, the conjugation between the beacons and dark quencher-labeled guard-DNA (G-DNA) quenches the fluorescent signals and turns biosensor to ‘Off’ state; finally, in presence of target protein (thrombin), the thrombin–aptamer binding and the release of G-DNA induce the restoration of fluorescent signals and turns biosensor back to ‘On’ state and the recovery efficiency ratio of fluorescent signal varies quantitatively with the concentration of the target protein. Owing to emission light guidance of the artificially designed 3D nanoporous PC structure, as far as we know, this proposed thrombin detection biosensor shows the highest sensitivity with a detection limit of 3.8 pM compared to other existing fluorescent based detection techniques.

Although membrane distillation (MD) has the potential for use in both desalination and industrial wastewater treatment, its widespread application has been hindered by its inherent limitations such as membrane fouling due to scale formation. One of the approaches to overcome this challenge is to develop novel membrane modules to mitigate the scale formation. Accordingly, this study sought to devise a way to control scale formation in MD systems using baffles in the modules. Moreover, the analysis based on computational fluid dynamics (CFD) was carried out together with bench-scale experiments to understand the hydrodynamic conditions in MD modules. CFD model was developed and applied to analyze flow velocity, shear stress, and vorticity. CFD analysis revealed that the baffled-modules can provide higher shear stress and generate both oscillatory mixing and vortices. Experimental results showed that fouling due to scale formation was more efficiently mitigated in the modules with the orifice baffles than the other modules. The final flux was increased from 2 L/m2-hr in the basic module to up to 19 L/m2-hr in the module with three orifice holes. According to the CFD results, it is attributed to the retardation of crystal deposition and cake formations by increased shear stress and vortices.

To date, there is no commercial point-of-care test (POCT) for intact virus detection. A POCT should show high sensitivity and have proper structure through simple and cost effective fabrication; moreover it should not require a complex and bulky instrument for measurement. In this study, a direct label-free biosensor based on 3D photonic crystal (PC) structures for Rotavirus has been demonstrated, which can be developed for POCT. The proposed sensor can detect the target with viral loading from 6.35 μg ml−1 to 1.27 mg ml−1 without any sample pretreatment. Sensitivity and selectivity performance are analyzed quantitatively by measuring peak wavelength value (PWV) and comparing the performance using an ELISA assay. In order to facilitate the target virus accessibility to inner structures in the sensor, the hydrogel based inverse opal structure is etched by O2 plasma and the connection window between nanopores becomes enlarged. This process is critical for enhancing virus sensing performance. Our PWV shift range is larger than of the shift in a 1D photonic crystal based virus sensor because the larger surface area in 3D inverse opal structures is realized. These results show that the proposed method is useful for developing a direct and easy-to-use virus detection kit in the form of a POCT in the near future.