The effective monitoring of microalgae cultivation is crucial for optimizing their energy utilization efficiency. In this paper, a quantitative analysis method, using microalgae images based on two convolutional neural networks, EfficientNet (EFF) and residual network (RES), is proposed. Suspension samples prepared from two types of dried microalgae powders, Rhodophyta (RH) and Spirulina (SP), were used to mimic real microalgae cultivation settings. The method's prediction accuracy of the algae concentration ranges from 0.94 to 0.99. RH, with a distinctively pronounced red-green-blue value shift, achieves a higher prediction accuracy than SP. The prediction results of the two algorithms were significantly superior to those of a linear regression. Additionally, RES outperforms EFF in terms of its generalization ability and robustness, which is attributable to its distinct residual block architecture. The RES provides a viable approach for the image-based quantitative analysis.
Biomass , Microalgae , Neural Networks, Computer , Spirulina , Microalgae/metabolism , Spirulina/metabolism , Rhodophyta/metabolism , Image Processing, Computer-Assisted/methods , Algorithms
More and more attention has been paid to condensable particulate matter (CPM) since its emissions have surpassed that of filterable particulate matter (FPM) with the large-scale application of ultralow-emission reform. CPM is a gaseous material in the flue stack but instantly turns into particles after leaving the stack. It is composed of inorganic and organic components. Organic components are an important part of CPM, and they are an irritant, teratogenic, and carcinogenic, which triggers photochemical smog, urban haze, and acid deposition. CPM organic components can aggravate air pollution and climate change; therefore, consideration should be given to them. Based on existing methods for removing atmospheric organic pollutants and combined with the characteristics of CPM organic components, we provide a critical overview from the aspects of (i) fundamental cognition of CPM, (ii) common methods to control CPM organic components, and (iii) catalytic oxidation of CPM organic components. As one of the most encouraging methods, catalytic oxidation is discussed in detail, especially in combination with selective catalytic reduction (SCR) technology, to meet the growing demands for multipollutant control (MPC). We believe that this review is inspiring for a fuller understanding and deeper exploration of promising approaches to control CPM organic components.
Air Pollutants , Particulate Matter , Air Pollution/prevention & control
Liquefaction of microalgae in ethanol offers an eco-friendly bio-oil alternative, but solvent recycling is crucial for sustainability due to extra costs. In this work, Chlorella vulgaris was liquefied in supercritical ethanol at 260 °C, and the solvent phase (SP) separated from bio-oil was recovered and reused. Five liquefaction cycles were performed at identical temperature and pressure conditions to investigate the effects on oil production and nitrogen transformation. The findings demonstrated a gradual increase in water content in recycled SP. Ethanol-water co-solvent as the reaction medium promoted the decomposition and re-polymerization of protein in raw material, thus increasing the bio-oil yield (76.84 %) and higher heating value (33.53 MJ/kg) to some extent. Simultaneously, the relative nitrogen content of bio-oil rose from 8.03 % to 8.52 %, predominantly in the form of nitrogen heterocycles. The potential pathway for nitrogen conversion was revealed, which establishes a theoretical basis for the subsequent denitrification of bio-oil.
Chlorella vulgaris , Microalgae , Solvents , Ethanol/metabolism , Nitrogen/metabolism , Microalgae/metabolism , Water/metabolism , Temperature , Biofuels , Biomass
Wearable sensors are quickly making their way into psychophysiological research, as they allow collecting data outside of a laboratory and for an extended period of time. The present tutorial considers fidelity of physiological measurement with wearable sensors, focusing on reliability. We elaborate on why ensuring reliability for wearables is important and offer statistical tools for assessing wearable reliability for between participants and within-participant designs. The framework offered here is illustrated using several brands of commercially available heart rate sensors. Measurement reliability varied across sensors and, more importantly, across the situations tested, and was highest during sleep. Our hope is that by systematically quantifying measurement reliability, researchers will be able to make informed choices about specific wearable devices and measurement procedures that meet their research goals.
Wearable Electronic Devices , Humans , Heart Rate/physiology , Reproducibility of Results , Psychophysiology
Phosphate as one of the most essential components of living systems, robust analytical techniques available for phosphate sensing in natural waters and soils are essential for monitoring and predicting water quality and agronomic evaluation of phosphate. Using cyclic voltammetry, a point-of-use electrochemical sensor zirconium dioxide/zinc oxide/multiple-wall carbon nanotubes/ammonium molybdate tetrahydrate/screen printed electrode (ZrO2/ZnO/MWCNTs/AMT/SPE) was applied to explore the electro-redox reaction of phosphomolybdate complexes on the surface of electrode, which produced a quantitative electrochemical response of phosphate anions. The modification of the electrode surface with ZrO2/ZnO/MWCNTs nanocomposites is able to generate the electroactive species via chemical reaction between molybdenum (Mo(VI)) and the targeted phosphate anions, leading to a sensitive detection technique for trace phosphate with a lower detection limit (LOD = 2.0 × 10-8 mol L-1), higher reproducibility, anti-interference, and precision in different soil sources. This system will be of great potential to advance the trace-level understanding of phosphate especially in field environmental analysis.
Nanotubes, Carbon , Electrochemical Techniques , Electrodes , Phosphates/analysis , Reproducibility of Results , Soil
Glyphosate, which has been widely reported to be a toxic pollutant, is often present at trace amounts in the environment. In this study, a novel copper-aluminum metal hydroxide doped graphene nanoprobe (labeled as CuAl-LDH/Gr NC) was first developed to construct a non-enzymatic electrochemical sensor for detection trace glyphosate. The characterization results showed that the synthesized CuAl-LDH had a high-crystallinity flowered structure, abundant metallic bands and an intercalated functional group. After mixed with Gr, the nanocomposites provided a larger surface area and better conductivity. The as-prepared CuAl-LDH/Gr NC dramatically improved the enrichment capability for glyphosate to realize the stripping voltammetry detection. The logarithmic linear detection range of the sensor was found to be 2.96 × 10-9-1.18 × 10-6 mol L-1 with the detection limit of 1 × 10-9 mol L-1 with excellent repeatability, good stability and anti-interference ability. Further, the sensor achieved satisfactory recovery rates in spiked surface water, ranging from 97.64% to 108.08%, demonstrating great accuracy and practicality.
The Internet of things (IoT) technology is developing rapidly, and the IoT services are penetrating broadly into every aspect of people’s lives. As the large amount of services grows dramatically, how to discover and select the best services dynamically to satisfy the actual needs of users in the IoT service set, the elements of which have the same function, is an unavoidable issue. Therefore, for the robustness of the IoT system, evaluating the quality level of the IoT service to provide a reference for the users choosing the most appropriate service has become a hot topic. Most of the current methods just use some static data to evaluate the quality of the service and ignore the dynamic changing trend of the service performance. In this paper, an estimation mechanism for the quality level of the IoT service based on fuzzy logic is conducted to grade the quality of the service. Specifically, the comprehensive factors are taken into account according to the defined level changing rules and the effect of the service in the previous execution process, so that it can provide users with an effective reference. Experiments are carried out by using a simulated service set. It is shown that the proposed algorithm can estimate the quality level of the service more comprehensively and reasonably, which is evidently superior to the other two common methods, i.e., the estimating method by a Randomization Test (RT) and the estimating method by a Single Test in Steps (STS).
