Even Visually Intact Cell Walls in Waterlogged Archaeological Wood Are Chemically Deteriorated and Mechanically Fragile : A Case of a 170 Year-Old Shipwreck

Structural and chemical deterioration and its impact on cell wall mechanics were investigated for visually intact cell walls (VICWs) in waterlogged archaeological wood (WAW). Cell wall mechanical properties were examined by nanoindentation without prior embedding. WAW showed more than 25% decrease of both hardness and elastic modulus. Changes of cell wall composition, cellulose crystallite structure and porosity were investigated by ATR-FTIR imaging, Raman imaging, wet chemistry, C-13-solid state NMR, pyrolysis-GC/MS, wide angle X-ray scattering, and N-2 nitrogen adsorption. VICWs in WAW possessed a cleavage of carboxyl in side chains of xylan, a serious loss of polysaccharides, and a partial breakage of beta -O-4 interlinks in lignin. This was accompanied by a higher amount of mesopores in cell walls. Even VICWs in WAW were severely deteriorated at the nanoscale with impact on mechanics, which has strong implications for the conservation of archaeological shipwrecks.Peer reviewe

Even Visually Intact Cell Walls in Waterlogged Archaeological Wood Are Chemically Deteriorated and Mechanically Fragile: A Case of a 170 Year-Old Shipwreck

Structural and chemical deterioration and its impact on cell wall mechanics were investigated for visually intact cell walls (VICWs) in waterlogged archaeological wood (WAW). Cell wall mechanical properties were examined by nanoindentation without prior embedding. WAW showed more than 25% decrease of both hardness and elastic modulus. Changes of cell wall composition, cellulose crystallite structure and porosity were investigated by ATR-FTIR imaging, Raman imaging, wet chemistry, 13C-solid state NMR, pyrolysis-GC/MS, wide angle X-ray scattering, and N2 nitrogen adsorption. VICWs in WAW possessed a cleavage of carboxyl in side chains of xylan, a serious loss of polysaccharides, and a partial breakage of β-O-4 interlinks in lignin. This was accompanied by a higher amount of mesopores in cell walls. Even VICWs in WAW were severely deteriorated at the nanoscale with impact on mechanics, which has strong implications for the conservation of archaeological shipwrecks

Mixing state and particle hygroscopicity of organic-dominated aerosols over the Pearl River Delta region in China

Simultaneous measurements of aerosol hygroscopicity and particle-phase chemical composition were performed at a suburban site over the Pearl River Delta region in the late summer of 2016 using a self-assembled hygroscopic tandem differential mobility analyzer (HTDMA) and an Aerodyne quadruple aerosol chemical speciation monitor (ACSM), respectively. The hygroscopic growth factor (HGF) of the Aitken mode (30 nm, 60 nm) and accumulation mode (100 nm, 145 nm) particles were obtained under 90% relative humidity (RH). An external mixture was observed for particles of every size during this study, with a dominant mode of more-hygroscopic (MH) particles, as aged aerosols dominated due to the anthropogenic influence. The HGF of lesshygroscopic (LH) mode particles increased, while their number fractions decreased during the daytime due to a reduced degree of external mixing that probably resulted from the condensation of gaseous species. These LH mode particles in the early morning or late afternoon could be possibly dominated by carbonaceous material emitted from local automobile exhaust during rush hours. During polluted days with air masses flowing mainly from the coastal areas, the chemical composition of aerosols had a clear diurnal variation and a strong correlation with the mean HGF. Closure analysis was carried out between the HTDMA-measured HGF and the ACSM-derived hygroscopicity using various approximations for the hygroscopic growth factor of organic compounds (HGF(org)). Considering the assumptions regarding the differences in the mass fraction of each component between PM1 and 145 nm particles, the hygroscopicity-composition closure was achieved using an HGF(org) of 1.26 for the organic material in the 145 nm particles and a simple linear relationship between the HGForg and the oxidation level inferred from the O : C ratio of the organic material was suggested. Compared with the results from other environments, HGF(org) obtained from our measurements appeared to be less sensitive to the variation of its oxidation level, which is, however, similar to the observations in the urban atmosphere of other megacities in China. This finding suggests that the anthropogenic precursors or the photooxidation mechanisms might differ significantly between the suburban and urban atmosphere in China and those in other background environments. This may lead to different characteristics of the oxidation products in secondary organic aerosols (SOA) and therefore to a different relationship between the HGF(org) and its O : C ratio.Peer reviewe

Optoelectronics with electrically tunable PN diodes in a monolayer dichalcogenide

One of the most fundamental devices for electronics and optoelectronics is the PN junction, which provides the functional element of diodes, bipolar transistors, photodetectors, LEDs, and solar cells, among many other devices. In conventional PN junctions, the adjacent p- and n-type regions of a semiconductor are formed by chemical doping. Materials with ambipolar conductance, however, allow for PN junctions to be configured and modified by electrostatic gating. This electrical control enables a single device to have multiple functionalities. Here we report ambipolar monolayer WSe2 devices in which two local gates are used to define a PN junction exclusively within the sheet of WSe2. With these electrically tunable PN junctions, we demonstrate both PN and NP diodes with ideality factors better than 2. Under excitation with light, the diodes show photodetection responsivity of 210 mA/W and photovoltaic power generation with a peak external quantum efficiency of 0.2%, promising numbers for a nearly transparent monolayer sheet in a lateral device geometry. Finally, we demonstrate a light-emitting diode based on monolayer WSe2. These devices provide a fundamental building block for ubiquitous, ultra-thin, flexible, and nearly transparent optoelectronic and electronic applications based on ambipolar dichalcogenide materials.Comment: 14 pages, 4 figure

The mechanisms of Yu Ping Feng San in tracking the cisplatin-resistance by regulating ATP-binding cassette transporter and glutathione S-transferase in lung cancer cells

Cisplatin is one of the first line anti-cancer drugs prescribed for treatment of solid tumors; however, the chemotherapeutic drug resistance is still a major obstacle of cisplatin in treating cancers. Yu Ping Feng San (YPFS), a well-known ancient Chinese herbal combination formula consisting of Astragali Radix, Atractylodis Macrocephalae Rhizoma and Saposhnikoviae Radix, is prescribed as a herbal decoction to treat immune disorders in clinic. To understand the fast-onset action of YPFS as an anti-cancer drug to fight against the drug resistance of cisplatin, we provided detailed analyses of intracellular cisplatin accumulation, cell viability, and expressions and activities of ATP-binding cassette transporters and glutathione S-transferases (GSTs) in YPFS-treated lung cancer cell lines. In cultured A549 or its cisplatin-resistance A549/DDP cells, application of YPFS increased accumulation of intracellular cisplatin, resulting in lower cell viability. In parallel, the activities and expressions of ATP-binding cassette transporters and GSTs were down-regulated in the presence of YPFS. The expression of p65 subunit of NF-κB complex was reduced by treating the cultures with YPFS, leading to a high ratio of Bax/Bcl-2, i.e. increasing the rate of cell death. Prim-O-glucosylcimifugin, one of the abundant ingredients in YPFS, modulated the activity of GSTs, and then elevated cisplatin accumulation, resulting in increased cell apoptosis. The present result supports the notion of YPFS in reversing drug resistance of cisplatin in lung cancer cells by elevating of intracellular cisplatin, and the underlying mechanism may be down regulating the activities and expressions of ATP-binding cassette transporters and GSTs

Deep-Learning-Enabled Fast Optical Identification and Characterization of Two-Dimensional Materials

Advanced microscopy and/or spectroscopy tools play indispensable role in nanoscience and nanotechnology research, as it provides rich information about the growth mechanism, chemical compositions, crystallography, and other important physical and chemical properties. However, the interpretation of imaging data heavily relies on the "intuition" of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, we use the optical characterization of two-dimensional (2D) materials as a case study, and demonstrate a neural-network-based algorithm for the material and thickness identification of exfoliated 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, segment sizes and their distributions, based on which we develop an ensemble approach topredict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other applications such as identifying layer numbers of a new 2D material, or materials produced by a different synthetic approach. Our artificial-intelligence-based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials and potentially accelerate new material discoveries

Machine Learning Models with Quantitative Wood Anatomy Data Can Discriminate between Swietenia macrophylla and Swietenia mahagoni

Illegal logging and associated trade aggravate the over-exploitation of Swietenia species, of which S. macrophylla King, S. mahagoni (L.) Jacq, and S. humilis Zucc. have been listed in Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Appendix Ⅱ. Implementation of CITES necessitates the development of efficient forensic tools to identify wood species accurately, and ideally ones readily deployable in wood anatomy laboratories across the world. Herein, a method using quantitative wood anatomy data in combination with machine learning models to discriminate between three Swietenia species is presented, in addition to a second model focusing only on the two historically more important species S. mahagoni and S. macrophylla. The intra- and inter-specific variations in nine quantitative wood anatomical characters were measured and calculated based on 278 wood specimens, and four machine learning classifiers—Decision Tree C5.0, Naïve Bayes (NB), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—were used to discriminate between the species. Among these species, S. macrophylla exhibited the largest intraspecific variation, and all three species showed at least partly overlapping values for all nine characters. SVM performed the best of all the classifiers, with an overall accuracy of 91.4% and a per-species correct identification rate of 66.7%, 95.0%, and 80.0% for S. humilis, S. macrophylla, and S. mahagoni, respectively. The two-species model discriminated between S. macrophylla and S. mahagoni with accuracies of over 90.0% using SVM. These accuracies are lower than perfect forensic certainty but nonetheless demonstrate that quantitative wood anatomy data in combination with machine learning models can be applied as an efficient tool to discriminate anatomically between similar species in the wood anatomy laboratory. It is probable that a range of previously anatomically inseparable species may become identifiable by incorporating in-depth analysis of quantitative characters and appropriate statistical classifiers

Path planning results are synthesized by the VBAS algorithm to avoid a single irregular obstacle.

(a) Motion results in a 2D plane. (b) Relationship between function and number of iterations.</p

Path planning results are synthesized by the VBAS algorithm to avoid a single regular obstacle.

(a) Motion results in a 2D plane. (b) Relationship between function and number of iterations.</p