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The characteristics of water vapor adsorption depend on the structure, porosity, and functional groups of the material. Metal–organic framework (MOF)-derived carbon (MDC) is a novel material that exhibits a high specific area and tunable pore sizes by exploiting the stable structure and porosity of pure MOF materials. Herein, two types of aluminum-based MOFs were used as precursors to synthesize hydrophobic microporous C-MDC and micro-mesoporous A-MDC via carbonization and activation depending on the type of ligands in the precursors. C-MDC and A-MDC have different pore characteristics and exhibit distinct water adsorption properties. C-MDC with hydrophobic properties and micropores exhibited negligible water adsorption (108.54 mgg−1) at relatively low pressures (P/P0~0.3) but showed a rapid increase in water adsorption ability (475.7 mgg−1) at relative pressures of about 0.6. A comparison with the isotherm model indicated that the results were consistent with the theories, which include site filling at low relative pressure and pore filling at high relative pressure. In particular, the Do–Do model specialized for type 5 showed excellent agreement.

Background and Aims: Time-lapse technology is an emerging assisted reproductive technology (ART) that enables simultaneous culture and monitoring of embryos in vitro during their development. It provides extra information on key events during embryo development, which can assist embryologists in selecting embryos alongside traditional morphology evaluations. Various imaging technologies have been utilised to visualise and evaluate embryos such as brightfield microscopy, darkfield microscopy, and Hoffman modulation contrast microscopy. Although these imaging technologies allow for non-invasive imaging of human embryos in a clinical setting, 3D imaging of a whole embryo with subcellular resolution remains a challenge. Method: We demonstrate 3D time-lapse label-free imaging of live mouse embryos using the 3D refractive index (RI) imaging technique. The proposed method is a quantitative phase imaging technique that measures multiple-intensity images of transmitted light through the embryos via axial scanning to reconstruct the 3D RI map of the embryos. Results: Using the proposed method, live mouse embryos were monitored at different developmental stages, ranging from 2-cell embryos to expanded blastocysts. The measured 3D RI maps of the developing embryos reveal 3D landscape of subcellular features inside the embryos (Fig. 1). In the quantitative assessment of embryonic development, various morphokinematic parameters were extracted from the measured 3D RI maps of the embryos. Conclusion: Based on the results, we envision that our method has broad applications where non-invasive and quantitative assessment of embryonic development is required.

The ultraviolet photochemical degradation process is widely applied in wastewater treatment due to its low cost, high efficiency and sustainability. In this study, a novel rotating flow reactor was developed for UV-initiated photochemical reactions. The reactor was run in a continuous flow mode, and the tangential installation of the inlet and outlet on the annular reactor improved reaction rates. Numerical modelling, which combined solute transport, radiation transfer and photochemical kinetic degradation processes, was conducted to evaluate improvement compared to current reactor designs. Methylene Blue (MB) decomposition efficiency from the modelling results and the experimental data agreed well with each other. The model results showed that a rotational motion of fluid was well developed inside the designed reactor for a wide range of inflow rates; the generation of ·OH radicals significantly depended on UV irradiation dose, and thus the degradation ratio of MB showed a strong correlation with the UV irradiation distribution. In addition, the comprehensive numerical modelling showed promising potential for the simulation of UV/H2O2 processes in rotating flow reactors.

The removal of nitrate (NO3−-N) and antibiotics in aquaculture tail water is urgent and necessary. A lab-scale up-flow immobilized biofilter (I-BF) filled with polyurethane foam (PUF) carriers and a microbial consortium was developed for simultaneous removal of nitrate and tetracycline (TC). The denitrification and TC removal performance of the I-BF reactor was investigated under different TC concentrations (0, 10, 50, 100 mg·L−1), carbon/nitrogen (C/N) ratio (2, 4, 5, 6) and hydraulic retention times (HRT) (4, 8, 12 h). Simultaneous removal of nitrogen and TC was achieved by the I-BF reactor. Low TC concentration (≤50 mg·L−1) had little effect on nitrogen removal. The denitrification performance of the I-BF reactor was inhibited at high TC load, which may be attributed to the damage of cell membranes and the inhibition of the intracellular denitrification enzymes’ activities. The optimal C/N ratio and HRT were 5 h and 8 h with almost complete denitrification and high TC removal efficiency (73.46%) at influent NO3−-N and TC concentrations of 100 mg·L−1 and 50 mg·L−1, respectively. The I-BF reactor proposed in this study has promising applications such as the treatment of piggery wastewater, aquaculture tail water and pharmaceutical wastewater co-contaminated with nitrate and antibiotics.

UV chemical degradation is a low-cost and sustainable wastewater treatment technology that protects the environment. In this study, computational fluid dynamics (CFD), mass transfer, and photochemical kinetic models combined with the continuous flow mode of UV/H2O2 were applied for the photochemical reaction of internal airlift circulation photocatalytic reactor to improve the efficiency of the reaction. Results show that with the increase in gas flow rate, the turbulence intensity and internal circulation effect of liquid in the reactor can be enhanced under the condition of constant baffle spacing. The CFD simulation prediction results of the chemical components in the liquid flow show that H2O2 has a high correlation with the OH radical formation, which depends on the intensity of UV. Thus, the degradation rate of methylene blue (MB) has a high correlation with UV intensity. The degradation efficiency of MB is improved with the increase in gas velocity by comparing the experimental data with the numerical simulation data. The experimental data are generally lower than the numerical prediction data, and although a certain difference range is observed, the overall prediction results are better.

Machine learning-based time-series forecasting has recently been intensively studied. Deep learning (DL), specifically deep neural networks (DNN) and long short-term memory (LSTM), are the popular approaches for this purpose. However, these methods have several problems. First, DNN needs a lot of data to avoid over-fitting. Without sufficient data, the model cannot be generalized so it may not be good for unseen data. Second, impaired data affect forecasting accuracy. In general, one trains a model assuming that normal data enters the input. However, when anomalous data enters the input, the forecasting accuracy of the model may decrease substantially, which emphasizes the importance of data integrity. This paper focuses on these two problems. In time-series forecasting, especially for photovoltaic (PV) forecasting, data from solar power plants are not sufficient. As solar panels are newly installed, a sufficiently long period of data cannot be obtained. We also find that many solar power plants may contain a substantial amount of anomalous data, e.g., 30%. In this regard, we propose a data preprocessing technique leveraging convolutional autoencoder and principal component analysis (PCA) to use insufficient data with a high rate of anomaly. We compare the performance of the PV forecasting model after applying the proposed anomaly detection in constructing a virtual power plant (VPP). Extensive experiments with 2517 PV sites in the Republic of Korea, which are used for VPP construction, confirm that the proposed technique can filter out anomaly PV sites with very high accuracy, e.g., 99%, which in turn contributes to reducing the forecasting error by 23%. [ABSTRACT FROM AUTHOR]