10 research outputs found

    Review of Typical Municipal Solid Waste Disposal Status and Energy Technology

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    AbstractThe physical and chemical components of urban domestic waste, medical waste and electronic waste are introduced, the advantages and disadvantages of the existing processing methods are pointed out. Considering the higher content of organic and metal of these municipal solid wastes, pyrolysis technology is a suitable method to make them harmless, reduced, reusable and being available energy

    Analysis and forecast of the Tianjin industrial carbon dioxide emissions resulted from energy consumption

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    This paper analyses the carbon dioxide emissions caused by industrial energy consumption of Tianjin from 2005 to 2012. The carbon emissions decomposition illustrated that the scale of production factor played a major role in the growth of Tianjin industrial carbon emissions and the average contribution of carbon emissions is up to 220.8975% in the statistical period; the intensity of energy factor played an important role in slowing down the growth of industrial carbon dioxide emissions. The average contribution of carbon emissions was −136.1994% in the statistical period. The prediction model based on carbon emissions data from industrial energy consumption from 2003 to 2012 reached a high accuracy, with an average error of 1.78% for stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, 2.41% for the Logistic regression model and an average error of 1.54% for the grey model. This research can contribute to predict the carbon emission and through it some suggestions can be made

    Multimodal prehabilitation to improve the clinical outcomes of frail elderly patients with gastric cancer: a study protocol for a multicentre randomised controlled trial (GISSG+2201)

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    Introduction Gastric cancer (GC) diagnosed in the elderly population has become a serious public health problem worldwide. Given the combined effects of frailty and the consequences of cancer treatment, older individuals with GC are more likely than young patients to suffer from postoperative complications and poor clinical outcomes. Nutrition, functional capacity and psychological state-based multimodal prehabilitation, which is dominated by Enhanced Recovery After Surgery (ERAS) pathway management, has been shown to reduce postoperative complications, promote functional recovery and decrease hospitalisation time in certain malignancies. However, no previous studies have investigated the clinical application of multimodal prehabilitation in frail older patients with GC.Methods and analysis The study is a prospective, multicentre randomised controlled trial in which a total of 368 participants who meet the inclusion criteria will be randomised into either a prehabilitation group or an ERAS group. The prehabilitation group will receive multimodal prehabilitation combined with ERAS at least 2 weeks before the gastrectomy is performed, including physical and respiratory training, nutritional support, and therapy and psychosocial treatment. The ERAS group patients will be treated according to the ERAS pathway. All interventions will be supervised by family members. The primary outcome measures are the incidence and severity of postoperative complications. Secondary outcomes include survival, functional capacity and other short-term postoperative outcomes. Overall, the multimodal prehabilitation protocol may improve functional capacity, reduce the surgical stress response and concomitant systemic inflammation, and potentially modulate the tumour microenvironment to improve short-term and long-term clinical outcomes and patients’ quality of life.Ethics and dissemination All procedures and participating centres of this study were approved by their respective ethics committees (QYFYKYLL 916111920). The final study results will be published separately in peer-reviewed journals.Trial registration number NCT05352802

    Arrays of horizontal carbon nanotubes of controlled chirality grown using designed catalysts

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    The semiconductor industry is increasingly of the view that Moore's law-which predicts the biennial doubling of the number of transistors per microprocessor chip-is nearing its end(1). Consequently, the pursuit of alternative semiconducting materials for nanoelectronic devices, including single-walled carbon nanotubes (SWNTs), continues(2-4). Arrays of horizontal nanotubes are particularly appealing for technological applications because they optimize current output. However, the direct growth of horizontal SWNT arrays with controlled chirality, that would enable the arrays to be adapted for a wider range of applications and ensure the uniformity of the fabricated devices, has not yet been achieved. Here we show that horizontal SWNT arrays with predicted chirality can be grown from the surfaces of solid carbide catalysts by controlling the symmetries of the active catalyst surface. We obtained horizontally aligned metallic SWNT arrays with an average density of more than 20 tubes per micrometre in which 90 per cent of the tubes had chiral indices of (12, 6), and semiconducting SWNT arrays with an average density of more than 10 tubes per micrometre in which 80 per cent of the nanotubes had chiral indices of (8, 4). The nanotubes were grown using uniform size Mo2C and WC solid catalysts. Thermodynamically, the SWNT was selectively nucleated by matching its structural symmetry and diameter with those of the catalyst. We grew nanotubes with chiral indices of (2m, m) (where m is a positive integer), the yield of which could be increased by raising the concentration of carbon to maximize the kinetic growth rate in the chemical vapour deposition process. Compared to previously reported methods, such as cloning(5,6), seeding(7,8) and specific-structure-matching growth(9-11), our strategy of controlling the thermodynamics and kinetics offers more degrees of freedom, enabling the chirality of as-grown SWNTs in an array to be tuned, and can also be used to predict the growth conditions required to achieve the desired chiralities.clos
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