Catalytic Pyrolysis of Kapok Fiber for Production of Olefins

Pyrolysis of kapok fibers over mesoporous molecular sieves of MCM-41, Zr-MCM-41 and Cr-MCM-41 (the mole ratio of Si:Zr or Si/Cr=50) was studied by using pyrolysis-gas chromatography-mass spectrometry (Py-GC/MS). Pure silicon MCM-41 showed weak acidity during pyrolysis with furfural as the main product. Zr-MCM-41 showed the dual-functionality of acid and base with both furfural and hydroxy acetone present in the products. Cr-MCM-41 was more acidic with more furfural produced. The optimal conditions for producing olefins were found to be 600°C and the ratio of kapok fiber to catalyst being 1:10 with the Zr-MCM-41 catalyst. The main products obtained via pyrolysis of kapok fiber were acetic acid, furfural, 2,4-di-tert-butylphenol, olefins, and alkanes. The excess of the catalyst and the high temperature of the reaction had certain effects on the pyrolysis of biomass to produce olefins, such as 1-decene, 1-dodecene, 1-undecene, 1-tridecene and heptadecane. Citation: Qiu, Q., Cai, Y., Ye, Q., and Lv, W. (2019). Catalytic Pyrolysis of Kapok Fiber for Production of Olefins. Trends in Renewable Energy, 5, 218-228. DOI: 10.17737/tre.2019.5.2.009

Optimizing adjuvant treatment options for patients with glioblastoma

BackgroundThis study focused on minimizing the costs and toxic effects associated with unnecessary chemotherapy. We sought to optimize the adjuvant therapy strategy, choosing between radiotherapy (RT) and chemoradiotherapy (CRT), for patients based on their specific characteristics. This selection process utilized an innovative deep learning method.MethodsWe trained six machine learning (ML) models to advise on the most suitable treatment for glioblastoma (GBM) patients. To assess the protective efficacy of these ML models, we employed various metrics: hazards ratio (HR), inverse probability treatment weighting (IPTW)-adjusted HR (HRa), the difference in restricted mean survival time (dRMST), and the number needed to treat (NNT).ResultsThe Balanced Individual Treatment Effect for Survival data (BITES) model emerged as the most effective, demonstrating significant protective benefits (HR: 0.53, 95% CI, 0.48–0.60; IPTW-adjusted HR: 0.65, 95% CI, 0.55–0.78; dRMST: 7.92, 95% CI, 7.81–8.15; NNT: 1.67, 95% CI, 1.24–2.41). Patients whose treatment aligned with BITES recommendations exhibited notably better survival rates compared to those who received different treatments, both before and after IPTW adjustment. In the CRT-recommended group, a significant survival advantage was observed when choosing CRT over RT (p < 0.001). However, this was not the case in the RT-recommended group (p = 0.06). Males, older patients, and those whose tumor invasion is confined to the ventricular system were more frequently advised to undergo RT.ConclusionOur study suggests that BITES can effectively identify GBM patients likely to benefit from CRT. These ML models show promise in transforming the complex heterogeneity of real-world clinical practice into precise, personalized treatment recommendations

Confidence Interval, Prediction Interval and Tolerance Interval for the Skew Normal Distribution: A Pivotal Approach

The class of skew normal distributions, introduced by Azzalini (1985), which is an asymmetric distribution and allows the presence of skewness. In this paper, we propose the pivotal quantity approach to construct the confidence interval for the mean, prediction interval for the mean of the future sample, and tolerance interval for the quantile. The fiducial distribution is also studied. Moreover, the performances of all the proposed confidence intervals are investigated through the Monte Carlo simulation. The pivotal quantity is a common method for calculating confidence intervals, which is used to construct confidence intervals in this paper. And the convergence of the obtained confidence interval is illustrated by the figures. Finally, a real data is used to explain proposed intervals in real life

Crystallization of liquid Cu nanodroplets on single crystal Cu substrates prefers closest-packed planes regardless of the substrate orientations

We report molecular dynamics simulations of thermal spray coating of Cu nanodroplets on Cu substrates with different orientations, and show that the droplets crystallize by adding the closest-packed {111} planes regardless of the substrate orientations. Such preferential growth along the closest-packed planes may be common in a broad range of crystallization and melting processes

Shock-induced consolidation and spallation of Cu nanopowders

A useful synthesis technique, shock synthesis of bulk nanomaterials from nanopowders, is explored here with molecular dynamics simulations. We choose nanoporous Cu (∼11 nm in grain size and 6% porosity) as a representative system, and perform consolidation and spallation simulations. The spallation simulations characterize the consolidated nanopowders in terms of spall strength and damage mechanisms. The impactor is full density Cu, and the impact velocity (ui) ranges from 0.2 to 2 km s−1. We present detailed analysis of consolidation and spallation processes, including atomic-level structure and wave propagation features. The critical values of ui are identified for the onset plasticity at the contact points (0.2 km s−1) and complete void collapse (0.5 km s−1). Void collapse involves dislocations, lattice rotation, shearing/friction, heating, and microkinetic energy. Plasticity initiated at the contact points and its propagation play a key role in void collapse at low ui, while the pronounced, grain-wise deformation may contribute as well at high ui. The grain structure gives rise to nonplanar shock response at nanometer scales. Bulk nanomaterials from ultrafine nanopowders (∼10 nm) can be synthesized with shock waves. For spallation, grain boundary (GB) or GB triple junction damage prevails, while we also observe intragranular voids as a result of GB plasticity