This work aimed to investigate the effect of pyrolysis temperature on the yield and properties of biochars synthesized from herbaceous and woody plants. Four typical materials, including two herbaceous plants (rice straw, corn straw) and two woody plants (camellia oleifera shells, garden waste), were used in the experiments under five operating temperatures (from 300 °C to 700 °C, with an interval of 100 °C). The results showed biochar derived from herbaceous plants had a significantly higher pH (from 7.68 to 11.29 for RS), electrical conductivity (EC, from 6.5 Ms cm−1 to 13.2 mS cm−1 for RS), cation exchange conductivity (CEC, from 27.81 cmol kg−1 to 21.69 cmol kg−1 for RS), and ash content (from 21.79% to 32.71% for RS) than the biochar from woody plants, but the volatile matter (VM, from 42.23% to 11.77% for OT) and specific surface area (BET, from 2.88 m2 g−1 to 301.67 m2 g−1 for OT) in the woody plant-derived biochar were higher. Except for CEC and VM, all the previously referred physicochemical characteristics in the as-prepared biochars increased with the increasing pyrolysis temperature, the H/C and O/C values of herbaceous and woody plant-derived biochar were lower than 0.9 and 0.3, respectively, confirming their potential as the material for carbon sequestration. The results revealed that biochar made from herbaceous plants was more suitable for acidic soil amendments. In contrast, woody plant-derived biochar were recommended to remove heavy metals in environmental remediation and water treatment. Graphical Abstract

Introduction Most adult cigarette smokers who use e-cigarettes are dual cigarette and e-cigarette (CC-EC) users, yet little is known about relative consumption of cigarettes to e-cigarettes and any associated harm reduction. Methods Rate of substitution from cigarettes to e-cigarettes at week 6 and change in biomarkers of exposure and potential harm were examined among dual CC-EC users [64/114 (56%); 35 Black, 29 Latino] in an e-cigarette switching randomized trial. Results Dual users averaged 79% substitution of cigarettes for e-cigarettes at week 6, resulting in a reduction from baseline of 70.0 ± 54.1 cigarettes per week (p < .001). Total nicotine consumption remained stable (baseline: 1160.5 ± 1042.1 pg/mL of cotinine, week 6: 1312.5 ± 1725.9 pg/mL of cotinine, p = .47), while significant reductions were seen in the potent lung carcinogen 4-(methylnitrosamino)-1-(3-pyridul)-1-butanol (NNAL) (-55.9 ± 88.6 ng/ml, p < .001), carbon monoxide (-6.3 ± 8.6 ppm, p < .001), and self-reported respiratory symptoms (-3.3 ± 8.0, p = .002). No significant changes were found in blood pressure or spirometry. Greater substitution from cigarettes to e-cigarettes was associated with larger reductions in NNAL (r = -.29, p = .02). Conclusions The predominant dual use pattern was characterized by regular e-cigarette and intermittent cigarette use. Findings demonstrate the short-term harm reduction potential of this dual use pattern in Black and Latino smokers and suggest that the greatest benefit, aside from cessation of both products, is achieved by higher substitution of e-cigarettes for cigarettes. Findings need confirmation in a larger sample with longer follow-up in dual users with greater variability in rate of substitution.

As the exploration of Mars and other solar system bodies becomes more prevalent, the importance of accurate methods in chemical analyses has increased. The use of laser‐induced breakdown spectroscopy (LIBS) in such analyses requires that well understood and accurate statistical methods exist for appropriate interpretation of resulting spectra. Many multivariate techniques have been developed for the elemental quantification of LIBS; however, each still has its limitations. In an endeavor to improve upon existing methodologies, a new algorithm is proposed using the ChemCam preflight calibration dataset and a dataset from the characterization of a LIBS/Raman sensor prototype developed at York University. The algorithm which was developed in this work is a linear mixture model within a submodel clustering framework. The cross validation and test results of the model on both datasets were reported using various metrics for each element under consideration (root mean square error, relative standard deviation, and R2 value). The algorithm was subsequently compared with other well established chemometric models on both datasets, such as principal component regression, partial least square regression, and ordinary least squares regression. Further validation of the algorithm was achieved by comparing the results presented herein to previously published results on the ChemCam data. The samples in each dataset are highly representative of Martian geology, which, given the overwhelming success of the algorithm on both datasets, suggests that subsequent implementation of the proposed algorithm on larger databases may have significant implications for Martian geochemical analyses and for planetary exploration as a whole.