4-[(2-Carb­oxy­eth­yl)amino]­benzoic acid monohydrate

In the title compound, C10H11NO4·H2O, the carboxyl group is twisted at a dihedral angle of 6.1 (3)° with respect to the benzene ring. In the crystal, the organic mol­ecules are linked by pairs of O—H⋯O hydrogen bonds involving both carboxyl groups, forming zigzag chains propagating along the b-axis direction. The water mol­ecules form [100] chains linked by O—H⋯O hydrogen bonds. The organic mol­ecule and water chains are cross-linked by N—H⋯Owater and Owater—H⋯O hydrogen bonds, generating (001) sheets

Synthesis, Properties, and Characterization of Field’s Alloy Nanoparticles and Its Slurry

This chapter describes a facile one-step method developed for the synthesis of Field’s alloy nanoparticles using a nanoemulsification technique and their dispersed them in a base fluid to make slurry. The composition, size, morphology, and thermal properties of as-prepared nanoparticles were characterized by XRF, TEM and, DSC, respectively. The slurry with Field’s alloy nanoparticles exhibited good thermal properties and stability. Meanwhile, an experimental study was performed to investigate the jet impingement of HFE7100 fluid with nanosized metallic (Field’s alloy) phase change materials (nano-PCM). Surface modification was used to stabilize the slurry of the nano-PCM in HFE7100 fluid and make the slurry stable for over 1 month. The Field’s alloy nano-PCM absorbed heat during a phase change process from solid to liquid phase coupled with HFE7100 evaporation process. The effects of mass fraction of Field’s alloy nano-PCM on the pressure drop and heat transfer performances of the slurry were investigated through a heat transfer loop test. Away from the critical heat flux, Field’s alloy nano-PCM slurry provided a significant heat transfer enhancement due to the increase in the thermal capacity of the carrier fluid. Moreover, the nano-PCM slurries were able to maintain 97% of their heat removal capability after 5000 thermal cycles

Experimental and Theoretical Analysis of the Smoke Layer Height in the Engine Room under the Forced Air Condition

The smoke layer height in the ship engine room under forced ventilation has been experimental and theoretical investigated in this work. A series of test were carried out in a scaled engine cabin experimental platform to obtain the influence of air supply volume and air inlet height on the burning parameters, including the mass loss rate, smoke temperature, etc. The research results show that under the experimental conditions, the fire source mass loss rate increases exponentially, and smoke layer height also increases gradually with the increase in the air supply volume. The empirical formula of smoke layer height under different air supply conditions was given. Then, a prediction model of smoke layer height under different forced ventilation conditions was constructed through theoretical analysis based on conservation equations. Within the range of experimental air volume and air inlet height, the relative error between theoretical prediction results and experimental results was less than 11%, which could effectively predict the smoke layer height in the ship cabin fire

Effect of pretreatment with alkali on the anaerobic digestion characteristics of kitchen waste and analysis of microbial diversity

Kitchen waste contains high contents of organic matter and moisture, and it is prone to biodegrade and decompose to give odors. If not collected and transported promptly or treated improperly, it is highly likely to pollute the environment and spread diseases. Because the lipid content in kitchen waste is high and a portion of organic matter is not subject to hydrolysis, the development of anaerobic digestion technology has been greatly limited. Kitchen waste was pretreated with NaOH, KOH, and Ca(OH)2 with different concentrations, and 50 days sequencing batch mesophilic anaerobic digestion experiments were conducted. This study sheds light on the pollution reduction and energy generation of kitchen waste. The results are as follows: (1) The lipid content of kitchen waste could be reduced, and the concentration of dissolved organic matter could be increased by pretreating with alkali. The degradation rate of kitchen waste lipid reached a maximum of 50.51%, if 3% NaOH was added, and the soluble chemical oxygen demand concentration was increased by 235.3%. (2) The cumulative methane (CH4) output and biogas production efficiency were improved in the anaerobic digestion process with kitchen waste pretreated with alkali. The maximum daily gas output of kitchen waste pretreated with NaOH and KOH took place on the 11th to 12th day, with the biogas production efficiency of 40.4 and 45.2 mL·g·VS−1. The cumulative CH4 output was increased from 370.2 mL·g·VS−1 (untreated) to 393.1 and 434.1 mL·g·VS−1, respectively. In addition, the concentration of CH4 in biogas was increased from 54.8% (untreated) to 59.1% and 61.7%, respectively. (3) The Chao1 and Ace values of bacteria were increased first and then decreased. On the 10th day, the diversity of bacteria reached the highest value, and on the 20th day, the diversity of archaea reached its maximum. Therefore, it was verified that the improvement in the hydrolysis acidification efficiency and degree was crucial for the rapid and complete anaerobic digestion reactions

The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction

This paper proposes a complete-information-based principal component analysis (CIPCA)-back-propagation neural network (BPNN)_ fault prediction method using real unmanned aerial vehicle (UAV) flight data. Unmanned aerial vehicles are widely used in commercial and industrial fields. With the development of UAV technology, it is imperative to diagnose and predict UAV faults and improve their safety and reliability. The data-driven fault prediction method provides a basis for UAV fault prediction. A UAV is a typical complex system. Its flight data is a kind of typical high-dimensional large sample dataset, and traditional methods cannot meet the requirements of data compression and dimensionality reduction at the same time. The method used interval data to compress UAV flight data, used CIPCA to reduce the dimensionality of the compressed data, and then used a back propagation (BP) neural network to predict UAV failure. Experimental results show that the CIPCA-BPNN method had obvious advantages over the traditional principal component analysis (PCA)-BPNN method and could accurately predict a failure about 9 s before the UAV failure occurred

Temperature sensitivity of decomposition of soil organic matter fractions increases with their turnover time

Soil organic carbon (SOC) is an indicator of soil fertility. Global warming accelerates SOC decomposition, consequently, resulting in land degradation. Characterization of the response of SOC decomposition to temperature is important for predicting land development. A simulation model based on temperature sensitivity (Q(10)) of SOC decomposition has been used to predict SOC response to climate warming. However, uncertain Q(10) leads to substantial uncertainties in the predictions. A major uncertainty comes from the interference of rainfall. To minimize this interference, we sampled surface (0-5 cm) soils along an isohyet across a temperature gradient in the Qinghai-Tibetan Plateau. The Q(10) of bulk soil and the four soil fractions, such as light fraction (LightF), particulate organic matter (POM), hydrolyzable fraction (HydrolysF), and recalcitrant fraction (RecalcitF), were studied by C-14 dating. Turnover time follows the order: LightF < POM < bulk soil < HydrolysF < RecalcitF. The Q(10) follows the order: LightF (1.0) = POM (1.0) < HydrolysF (3.63) < bulk soil (5.93) < RecalcitF (7.46). This indicates that stable fractions are much more sensitive to temperature than labile fractions. We also suggest that protection mechanisms rather than molecular composition regulate SOC turnover. A new concept 'protection sensitivity' of SOC decomposition was proposed. Protection sensitivity relates to protection type and primarily controls Q(10) variation. A simulation model based on the Q(10) of individual fractions predicted SOC change and land development in the Qinghai-Tibetan Plateau in the next 100 years much effectively as compared to simulations based on one-pool model (Q(10) = 2) or bulk soil (Q(10) = 5.93)

Effects of simulated extreme precipitation flooding on the degradation of anaerobic digestion effluent by algal-bacterial symbiosis system

Precipitation is directly affected by global warming among different extreme climate types. The moisture-holding capacity of the atmosphere increases by 7% with a temperature increase of 1 °C. A higher moisture content results in frequent and violent rainfall events, and the frequency and intensity of heavy rainfall and flooding events in certain countries and regions is gradually aggravated. Simulated extreme precipitation experiments with precipitation intensities of 50, 100, and 200 mm and precipitation time on 3th, 6 th, and 9 th day were set to study the effects of extreme precipitation and flooding events on the degradation performance of open algae–bacteria symbiotic (ABS) system for anaerobic digestion effluent (ADE). The experimental results showed that the precipitation time had an obvious impact on the open algae–bacteria symbiotic system. The daily COD (chemical oxygen demand) degradation rates in the algae–bacteria symbiotic system on the fourth day decreased from 12.64% (unaffected by precipitation) to 2.94, 2.38, and 1.90%, respectively, under the conditions of rainfall on the third day and precipitation intensities of 50, 100, and 200 mm. At the end of the experiments, the COD contents in the ADE increased from 355.09 mg/L (unaffected by precipitation) to 435.77, 413.67, and 403.54 mg/L, respectively. The degradation effects of COD, TN (total nitrogen), and TP (total phosphorus) in the open algae–bacteria symbiotic system were affected by extreme precipitation (200 mm) and flooding. On the third day of the heavy rainfall event, Actinobacteria, Proteobacteria, and Bacteroidetes were the most dominant phyla in the samples of the symbiotic system between algae and bacteria. Among them, the abundance of Proteobacteria and Bacteroidetes was the highest, accounting for 15.33, 21.33 and 19.97%, respectively. As the symbiotic system operated stably, their proportions decreased to 13.58, 12.13, and 17.85% on the ninth day. At the class level, the abundance of Beta-proteobacteria and Gamma-proteobacteria was relatively high in the samples of the symbiotic system. On the third day of the heavy rainfall event, they accounted for 8.99 and 7.11%, respectively. With the stable operation of the system, their proportions showed a decreasing trend, and on the sixth and ninth days, they were 7.03% and 6.01, 4.87 and 4.39%, respectively