Friction stir welding/processing of metals and alloys: A comprehensive review on microstructural evolution

In recent years, marine, freshwater and terrestrial pollution with microplastics has been discussed extensively, whereas atmospheric microplastic transport has been largely overlooked. Here, we present global simulations of atmospheric transport of microplastic particles produced by road traffic (TWPs – tire wear particles and BWPs – brake wear particles), a major source that can be quantified relatively well. We find a high transport efficiencies of these particles to remote regions. About 34% of the emitted coarse TWPs and 30% of the emitted coarse BWPs (100 kt yr−1 and 40 kt yr−1 respectively) were deposited in the World Ocean. These amounts are of similar magnitude as the total estimated direct and riverine transport of TWPs and fibres to the ocean (64 kt yr−1). We suggest that the Arctic may be a particularly sensitive receptor region, where the light-absorbing properties of TWPs and BWPs may also cause accelerated warming and melting of the cryosphere. Plastic pollution is a critical concern across diverse ecosystems, yet most research has focused on terrestrial and aquatic transport, neglecting other mechanisms. Here the authors show that atmospheric transport is a major pathway for road plastic pollution over remote regions.

/pdf/atmospheric-transport-is-a-major-pathway-of-microplastics-to-392w8pf6vz.pdf

Atmospheric transport is a major pathway of microplastics to remote regions

Water quality monitoring is an important component of water resources management. In order to predict two water quality variables, namely dissolved oxygen (DO; mg/L) and chlorophyll-a (Chl-a; µg/L) in the Small Prespa Lake in Greece, two standalone deep learning (DL) models, the long short-term memory (LSTM) and convolutional neural network (CNN) models, along with their hybrid, the CNN–LSTM model, were developed. The main novelty of this study was to build a coupled CNN–LSTM model to predict water quality variables. Two traditional machine learning models, support-vector regression (SVR) and decision tree (DT), were also developed to compare with the DL models. Time series of the physicochemical water quality variables, specifically pH, oxidation–reduction potential (ORP; mV), water temperature (°C), electrical conductivity (EC; µS/cm), DO and Chl-a, were obtained using a sensor at 15-min intervals from June 1, 2012 to May 31, 2013 for model development. Lag times of up to one (t − 1) and two (t − 2) for input variables pH, ORP, water temperature, and EC were used to predict DO and Chl-a concentrations, respectively. Each model’s performance in both training and testing phases was assessed using statistical metrics including the correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), their normalized equivalents (RRMSE, RMAE; %), percentage of bias (PBIAS), Nash–Sutcliffe coefficient ($$E_{NS}$$), Willmott’s Index, and graphical plots (Taylor diagram, box plot and spider diagram). Results showed that LSTM outperformed the CNN model for DO prediction, but the standalone DL models yielded similar performances for Chl-a prediction. Generally, the hybrid CNN–LSTM models outperformed the standalone models (LSTM, CNN, SVR and DT models) in predicting both DO and Chl-a. By integrating the LSTM and CNN models, the hybrid model successfully captured both the low and high levels of the water quality variables, particularly for the DO concentrations.

Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model

Empirical evidence for the response of soil carbon cycling to the combined effects of warming, drought and diversity loss is scarce. Microbial carbon use efficiency (CUE) plays a central role in regulating the flow of carbon through soil, yet how biotic and abiotic factors interact to drive it remains unclear. Here, we combine distinct community inocula (a biotic factor) with different temperature and moisture conditions (abiotic factors) to manipulate microbial diversity and community structure within a model soil. While community composition and diversity are the strongest predictors of CUE, abiotic factors modulated the relationship between diversity and CUE, with CUE being positively correlated with bacterial diversity only under high moisture. Altogether these results indicate that the diversity × ecosystem-function relationship can be impaired under non-favorable conditions in soils, and that to understand changes in soil C cycling we need to account for the multiple facets of global changes.

/pdf/microbial-diversity-drives-carbon-use-efficiency-in-a-model-3f2uox7jwf.pdf

Microbial diversity drives carbon use efficiency in a model soil

Biochar: Production, properties and emerging role as a support for enzyme immobilization

For forty years, there has been a widely held belief that over 2,000 years ago the Chinese Qin developed an advanced chromate conversion coating technology (CCC) to prevent metal corrosion. This belief was based on the detection of chromium traces on the surface of bronze weapons buried with the Chinese Terracotta Army, and the same weapons’ very good preservation. We analysed weapons, lacquer and soils from the site, and conducted experimental replications of CCC and accelerated ageing. Our results show that surface chromium presence is correlated with artefact typology and uncorrelated with bronze preservation. Furthermore we show that the lacquer used to cover warriors and certain parts of weapons is rich in chromium, and we demonstrate that chromium on the metals is contamination from nearby lacquer after burial. The chromium anti-rust treatment theory should therefore be abandoned. The good metal preservation probably results from the moderately alkaline pH and very small particle size of the burial soil, in addition to bronze composition.

/pdf/surface-chromium-on-terracotta-army-bronze-weapons-is-52hgvkc40y.pdf

Surface chromium on Terracotta Army bronze weapons is neither an ancient anti-rust treatment nor the reason for their good preservation

Abstract In this paper, X-ray diffraction spectroscopy (XRD), Raman spectroscopy (RS), firing temperature analysis, pore size distribution analysis, Fourier infrared spectroscopy (FTIR), scanning electron microscopy-energy dispersive spectroscopy (SEM–EDS), and transmission electron microscopy (TEM) were utilized to analyze the unearthed vulnerable terracotta figures of Emperor Qin Shihuang's Terracotta Army, as well as the unknown precipitated white crystals and black substances. The results show that the firing temperature of the vulnerable terracotta figure is much lower than those of similar terracotta figures and red terracotta figures with good texture. The pore size of this kind of vulnerable pottery is smaller and the specific surface area is larger. In the pores, there is an alkaline solution containing Ca 2+ that accounted for approximately 4.3% of the total mass of the sample. Along with the fracture of the delicate pottery matrix, the solution in the pores migrated to the fracture surface and chemically reacted with CO 2 in the air, generating a layer of white calcium carbonate crust. The black matter on the fracture surface of the vulnerable terracotta figure is mainly composed of amorphous carbon and graphitized carbon, which shows high activity under the catalysis of Fe and Ti elements and alkaline soil. The discovery and analysis of amorphous carbon, graphitized carbon, and white calcium carbonate crystals on the fracture surface of the Qin Shihuang’s Terracotta Army provide a theoretical foundation for in-depth analysis of the terracotta warriors and horses' composition and structure, as well as scientific protection and restoration, and the stability of the original site display. 

https://heritagesciencejournal.springeropen.com/counter/pdf/10.1186/s40494-022-00701-w

Analysis of newly discovered substances on the vulnerable Emperor Qin Shihuang's Terracotta Army figures

Abstract The striking realism of the life-sized ceramic terracotta warriors has been attracting the interest of the public and archaeologists since they were discovered from the mausoleum complex of the first Chinese Emperor Qin Shihuang in the 1970s. It is still debated whether the life-size models were based on individual people or were just crafted from the standardized models. This research examined the facial features of the terracotta warriors in a quantitative and contactless way with the support of the High-precision 3D point cloud modelling technology and the anthropometric method. The similarities and dissimilarities were analyzed among the facial features of terracotta warriors and 29 modern Chinese ethnic groups using mathematical statistics methods such as MDS, ANOVA, ranking analysis and cluster analysis. The results reveal that the features of the terracotta warriors highly resemble those of contemporary Chinese people and indicate that terracotta warriors were crafted from real portraits and intended to constitute a real army to protect the Emperor Qin Shihuang in the afterlife. 

/pdf/measurement-and-analysis-of-facial-features-of-terracotta-1n3wtpxk.pdf

Desheng Lan

Papers

Surface chromium on Terracotta Army bronze weapons is neither an ancient anti-rust treatment nor the reason for their good preservation

Analysis of newly discovered substances on the vulnerable Emperor Qin Shihuang's Terracotta Army figures

Measurement and analysis of facial features of terracotta warriors based on high-precision 3D point clouds