Explaining high flow rate of water in carbon nanotubes via solid-liquid molecular interactions

Experimental and simulation measurements of water flow through carbon nanotubes have shown orders of magnitude higher flow rates than what was predicted using continuum fluid mechanics models. Different explanations have been offered, from slippage of water on the hydrophobic surface of the nanotubes to size confinement effects. In this work a model capable of explaining these observations, linking the enhanced flow rates observed to the solid–liquid molecular interactions at the nanotube wall is proposed. The model is capable of separating the effects on flow enhancement of the tube characteristic dimensions and the solid–liquid molecular interactions, accurately predicting the effect of each component for nanotubes of different sizes, wall surface chemistry and structure. Comparison with the experimental data available shows good agreement

A General Framework for Uncertainty Estimation in Deep Learning

Neural networks predictions are unreliable when the input sample is out of the training distribution or corrupted by noise. Being able to detect such failures automatically is fundamental to integrate deep learning algorithms into robotics. Current approaches for uncertainty estimation of neural networks require changes to the network and optimization process, typically ignore prior knowledge about the data, and tend to make over-simplifying assumptions which underestimate uncertainty. To address these limitations, we propose a novel framework for uncertainty estimation. Based on Bayesian belief networks and Monte-Carlo sampling, our framework not only fully models the different sources of prediction uncertainty, but also incorporates prior data information, e.g. sensor noise. We show theoretically that this gives us the ability to capture uncertainty better than existing methods. In addition, our framework has several desirable properties: (i) it is agnostic to the network architecture and task; (ii) it does not require changes in the optimization process; (iii) it can be applied to already trained architectures. We thoroughly validate the proposed framework through extensive experiments on both computer vision and control tasks, where we outperform previous methods by up to 23% in accuracy.Comment: Accepted for publication in the Robotics and Automation Letters 2020, and for presentation at the International Conference on Robotics and Automation (ICRA) 202

Highly selective, iron-driven CO2 methanation

CO2 methanation has gained traction for its potential in renewable energy storage, though the high cost of renewable hydrogen production remains a significant barrier to implementation. Herein we present the Ru‐Fe@NCNT catalyst, consisting of ruthenium and iron nanoparticles on nitrogen‐doped carbon nanotubes, as a highly selective, hydrogen efficient, iron‐driven alternative to typical nickel and ruthenium catalysts used for CO and CO2 methanation. Ru‐Fe@NCNTs offer competitive CO2 conversion, improved methane selectivity, 26% higher hydrogen utilisation and an up to 80% reduction in ruthenium loading versus similar literature and commercial catalysts. It is proposed that this desirable CO2 methanation performance is a result of effective cooperation between the iron‐catalysed reverse water gas shift and methane‐selective Fischer‐Tropsch, and ruthenium‐catalysed CO methanation reactions

Options-based systemic risk, financial distress, and macroeconomic downturns

In this study, we propose an implied forward-looking measure for systemic risk that employs the information from put option prices, the Systemic Options Value-at-Risk (SOVaR). This new measure can capture the buildup stage of systemic risk in the financial sector earlier than the standard stock market-based systemic risk measures (SRMs). Non-parametric tests show that our measure exhibits more timely early warning signals (up to one month earlier) regarding the main turbulent events around the global financial crisis of 2007-2009 than the three main stock market-based SRMs. Moreover, this new measure also shows significant predictive power with respect to macroeconomic downturns as well as future recessions. Our results are robust to various specifications, breakdowns of financial sectors, and controlling for the other main risk measures proposed in the literature

Electro-osmotic flow enhancement in carbon nanotube membranes

In this work, experimental evidence of the presence of electro-osmotic flow (EOF) in carbon nanotube membranes with diameters close to or in the region of electrical double layer overlap is presented for two different electrolytes for the first time. No EOF in this region should be present according to the simplified theoretical framework commonly used for EOF in micrometre-sized channels. The simplifying assumptions concern primarily the electrolyte charge density structure, based on the Poisson-Boltzmann (P-B) equation. Here, a numerical analysis of the solutions for the simplified case and for the nonlinear and the linearized P-B equations is compared with experimental data. Results show that the simplified solution produces a significant deviation from experimental data, whereas the linearized solution of the P-B equation can be adopted with little error compared with the full P-B case. This work opens the way to using electro-osmotic pumping in a wide range of applications, from membrane-based ultrafiltration and nanofiltration (as a more efficient alternative to mechanical pumping at the nanoscale) to further miniaturization of lab-on-a-chip devices at the nanoscale for in vivo implantation.</p

5,5'(Oxy-bis(methylene)bis-2-furfural (OBMF) from 5-hydroxymethyl-2-furfural (HMF): a systematic study for the synthesis of a new platform molecule from renewable substances

The continued exploitation and depletion of fossil fuels has prompted the scientific community to search for more sustainable and environmentally friendly alternatives. In the last decade, the synthesis of biomass-derived chemicals has become a priority to boost the transition from refinery to biorefinery. Sugars are an extremely abundant bio-resource in nature; even today, one of the most studied reactions is the synthesis of 5-hydroxymethyl-2-furfural (HMF). This compound is considered extremely important for biorefinery because of its wide range of possible applications (pharmaceutical, biofuels, polymer precursors, surfactants). However, it has been observed, during the spontaneous degenerative process of HMF, the formation of a compound that could be equally important 5,5'-[oxybis(methylene)]bis-2-furfural (OBMF). The synthesis of OBMF is scarcely reported in the literature, only in recent years interest in this dimer of HMF has emerged for its possible applications in industry. Good yield values of OBMF are reported in the literature from HMF (Figure 1) in the presence of an acid catalyst; however, the solvents used are the most common halogenated and/or aromatic solvents, known to be toxic. The objective of this work was to find a viable synthetic route to access OBMF without having to resort to the use of such solvents and, in addition, utilize already commercially available and inexpensive acid catalysts. Through smallscale optimizations, the best solvent was found to be dimethyl carbonate;4 In addition, two heterogeneous acid catalysts - Purolite 269 and ferric sulfate (Fe2(SO4)3) - showed excellent efficiency in promoting the HMF etherification reaction with quantitative yields (&gt; 90%). Subsequently, a scale-up of the reaction was carried out, obtaining OBMF with an isolated yield of 81%. Given the excellent results obtained, this work can be a starting point to undertake the study of new synthetic methodologies for this molecule such as continuous flow reactions of which the literature is lacking

Dimethyl isosorbide via organocatalyst N-methyl pyrrolidine: scaling up, purification and concurrent reaction pathways

Dimethyl isosorbide (DMI) is a green replacement for conventional dipolar solvents as dimethyl sulfoxide (DMSO) and dimethylformamide (DMF) that are toxic and dangerous for human and environmental health. DMI is one of the simplest derivatives well-known bio-based platform chemical isosorbide, an anhydro sugar readily synthesised by D-sorbitol dehydration reaction. [1] The synthesis of DMI is mainly based on the etherification of bio-based platform chemical isosorbide in the presence of basic or acid catalyst employing different alkylating agent. Among them, dimethyl carbonate (DMC) is relevant thanks to its haracteristics: good biodegradability and low toxicity. [2] In this work, we report an extensive investigation on highly yielding methylation of isosorbide via DMC chemistry promoted by several nitrogen organocatalyst. [3] Reaction conditions were performed and then applied for the methylation of isosorbide epimers - isomannide and isoidide - and for preliminary scale-up test (10 g of isosorbide). Pure DMI, starting from mixture reaction, was obtained by both column chromatography and distillation at reduced pressure. Between all nitrogen used, N-methyl pyrrolidine (NMPy) demonstrated excellent behaviour as catalyst also for the one-pot conversion of D-sorbitol into DMI. Furthermore, for the first time, all seven methyl and carboxymethyl intermediates - observed during the etherification of isosorbide - were synthetized, isolated and characterised. This study allowed us to know more deeply the concurrent reaction pathways (methylation, methyl carbonylation and decarboxylation) leading to DMI and on the role played by NMPy in the methylation of isosorbide and in this way to propose a mechanism of conversion into isosorbide into DMI via DMC chemistry