Liquid fuels from biomass: An energy self-sustained process integrating H2 recovery and liquid refining

[EN] In the last years the research activities on biomass valorisation (mainly residues from urban and agricultural activities) have been intensified. Biomass is an abundant resource for energy generation and its extensive utilization may make possible to fulfil the goals determined by the national and international regulations about renewable sources and greenhouse gas emissions. In this work, simulations are carried out using ASPEN PLUS for an integrated process to produce liquid fuels from biomass in a self-sustainable energetic regime (thermal and electric) and several process factors have been considered. The process initially combines a primary pyrolysis reactor associated to a (char + gases) gasification unit in order to optimize the biomass use, followed by downstream processes to enhance the quality of final liquid fuel. The factors studied were the composition of the biomass, the primary (or pyrolytic) liquid yield, the composition of the liquid fuel, as well as the amount of the oxidant and steam used in the primary char-gasifier reactor. The use of a simplified model for liquid fuel composition let us to stablish a range of operational conditions in which both thermal and electric balance of the process are favourable. In this sense, the maximum extraction of liquid fuel was found around 20-25% by working at 10-25% of O-2 (as pure oxygen or air) and 15-45% of steam in the gasifier and fulfilling self-sustainable process condition, while biomass should possess C/O weight ratios >= 1.Financial support by the Spanish Government (ENE2014-57651, CTQ2015-67592, and SEV-2016-0683 grants) is gratefully acknowledged.D. Catalán-Martínez; Domine ., ME.; Serra Alfaro, JM. (2018). Liquid fuels from biomass: An energy self-sustained process integrating H2 recovery and liquid refining. Fuel. 212:353-363. https://doi.org/10.1016/j.fuel.2017.10.014S35336321

Impact of intercepted and sub-canopy snow microstructure on snowpack response to rain-on-snow events under a boreal canopy

Rain-on-snow events can cause severe flooding in snow-dominated regions. These are expected to become more frequent in the future as climate change shifts the precipitation from snowfall to rainfall. However, little is known about how winter rainfall interacts with an evergreen canopy and affects the underlying snowpack. In this study, we document 5 years of rain-on-snow events and snowpack observations at two boreal forested sites of eastern Canada. Our observations show that rain-on-snow events over a boreal canopy lead to the formation of melt–freeze layers as rainwater refreezes at the surface of the sub-canopy snowpack. They also generate frozen percolation channels, suggesting that preferential flow is favoured in the sub-canopy snowpack during rain-on-snow events. We then used the multi-layer snow model SNOWPACK to simulate the sub-canopy snowpack at both sites. Although SNOWPACK performs reasonably well in reproducing snow height (RMSE = 17.3 cm), snow surface temperature (RMSE = 1.0 °C), and density profiles (agreement score = 0.79), its performance declines when it comes to simulating snowpack stratigraphy, as it fails to reproduce many of the observed melt–freeze layers. To correct for this, we implemented a densification function of the intercepted snow in the canopy module of SNOWPACK. This new feature allows the model to reproduce 33 % more of the observed melt–freeze layers that are induced by rain-on-snow events. This new model development also delays and reduces the snowpack runoff. In fact, it triggers the unloading of dense snow layers with small rounded grains, which in turn produces fine-over-coarse transitions that limit percolation and favour refreezing. Our results suggest that the boreal vegetation modulates the sub-canopy snowpack structure and runoff from rain-on-snow events. Overall, this study highlights the need for canopy snow property measurements to improve hydrological models in forested snow-covered regions.</p

Bimetallic Intersection in PdFe@FeOx-C Nanomaterial for Enhanced Water Splitting Electrocatalysis

Supported Fe-doped Pd-nanoparticles (NPs) are prepared via soft transfor-mation of a PdFe-metal oraganic framework (MOF). The thus synthesized bimetallic PdFe-NPs are supported on FeOx@C layers, which are essential for developing well-defined and distributed small NPs, 2.3 nm with 35% metal loading. They are used as bifunctional nanocatalysts for the electro-catalytic water splitting process. They display superior mass activity for the oxygen evolution reaction (OER) and the hydrogen evolution reaction (HER), both in alkaline and acid media, compared with those obtained for benchmarking platinum HER catalyst, and ruthenium, and iridium oxide OER catalysts. PdFe-NPs also exhibit outstanding stability against sintering that can be explained by the protecting role of graphitic carbon layers provided by the organic linker of the MOF. Additionally, the superior electrocatalytic performance of the bimetallic PdFe-NPs compared with those of monometallic Pd/C NPs and FeOx points to a synergetic effect induced by Fe-Pd interactions that facilitates the water splitting reaction. This is supported by additional characterization of the PdFe-NPs prior and post electrolysis by TEM, XRD, X-ray photoelectron spectroscopy, and Raman revealing that dispersed PdFe NPs on FeOx@C promote interactions between Pd and Fe, most likely to be Pd-O-Fe active centers

MOF-mediated synthesis of supported Fe-doped Pd nanoparticles under mild conditions for magnetically recoverable catalysis

Metal-organic framework (MOF)-driven synthesis is considered as a promising alternative for the development of new catalytic materials with well-designed active sites. This synthetic approach is used here to gradually transform a new bimetallic MOF, with Pd and Fe as the metal components, by the in situ generation of aniline under mild conditions. This methodology results in a compositionally homogeneous nanocomposite formed by Fe-doped Pd nanoparticles that, in turn, are supported on iron oxide-doped carbon. The nanocomposite has been fully characterized by several techniques such as IR and Raman spectroscopy, TEM, XPS, and XAS. The performance of this nanocomposite as an heterogeneous catalyst for hydrogenation of nitroarenes and nitrobenzene coupling with benzaldehyde has been evaluated, proving it to be an efficient and reusable catalyst

Organics in environmental ices: sources, chemistry, and impacts

International audienceThe physical, chemical, and biological processes involving organics in ice in the environment impact a number of atmospheric and biogeochemical cycles. Organic material in snow or ice may be biological in origin, deposited from aerosols or atmospheric gases, or formed chemically in situ. In this manuscript, we review the current state of knowledge regarding the sources, properties, and chemistry of organic materials in environmental ices. Several outstanding questions remain to be resolved and fundamental data gathered before an accurate model of transformations and transport of organic species in the cryosphere will be possible. For example, more information is needed regarding the quantitative impacts of chemical and biological processes, ice morphology, and snow formation on the fate of organic material in cold regions. Interdisciplinary work at the interfaces of chemistry, physics and biology is needed in order to fully characterize the nature and evolution of organics in the cryosphere and predict the effects of climate change on the Earth's carbon cycle

The specific surface area and chemical composition of diamond dust near Barrow, Alaska

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95687/1/jgrd17349.pd

Exploring the decision-making process in model development: focus on the Arctic snowpack

The Arctic poses many challenges to Earth System and snow physics models, which are unable to simulate crucial Arctic snowpack processes, such as vapour gradients and rain-on-snow-induced ice layers. These limitations raise concerns about the current understanding of Arctic warming and its impact on biodiversity, livelihoods, permafrost and the global carbon budget. Recognizing that models are shaped by human choices, eighteen Arctic researchers were interviewed to delve into the decision-making process behind model construction. Although data availability, issues of scale, internal model consistency, and historical and numerical model legacies were cited as obstacles to developing an Arctic snowpack model, no opinion was unanimous. Divergences were not merely scientific disagreements about the Arctic snowpack, but reflected the broader research context. Inadequate and insufficient resources partly driven by short-term priorities dominating research landscapes, impeded progress. Nevertheless, modellers were found to be both adaptable to shifting strategic research priorities – an adaptability demonstrated by the fact that interdisciplinary collaborations were the key motivation for model development – and anchored in the past. This anchoring led to diverging opinions about whether existing models are “good enough” and whether investing time and effort to build a new model was a useful strategy when addressing pressing research challenges. Moving forward, we recommend that both stakeholders and modellers be involved in future snow model intercomparison projects in order to drive developments that address snow model limitations that currently impede progress in various disciplines. We also argue for more transparency about the contextual factors that shape research decisions. Otherwise, the reality of our scientific process will remain hidden, limiting the changes necessary to our research practice

Assessment of the psychosocial and economic impact according to sex in non-small cell lung cancer patients: an exploratory longitudinal study

Background: Little is known about the impact of sex on lung cancer patients from the psychological, economic and social perspectives. This study was designed to explore the psychosocial and economic impact according to sex of metastatic non-small cell lung cancer (mNSCLC) in patients and caregivers. Methods: Exploratory study of two cohorts of patients starting first-line treatment for mNSCLC. The following questionnaires were administered at baseline, 4 months later and following the first and second disease progression: APGAR, relationship impact scale, DUKE-UNC scale, economic impact in patients and caregiver, and Zarit scale. It was planned to include 1250 patients to get an 80% possibility of detecting as significant (p < 0.05) effect sizes less than 0.19 between men and women. Univariate comparisons were made between the tests applied to men and women. Overall survival was estimated with Kaplan–Meier method. Cox analyses were done to estimate hazard ratios (HRs) with 95% CI. Results: 333 patients were included. Most families reported to continue being functional despite the lung cancer diagnosis. Regardless of sex, they did not perceive changes in their partner relationship. Most patients felt their social support was normal. Roughly 25% of people reported a worsening in their economic situation, without remarkable differences by sex. Statistically significant differences were found between both groups regarding the caregiver’s relationship to the patient (more parents were the caregiver in females than in males, p < 0.0001) and the caregiver’s employment situation (more employed caregivers in females) (p < 0.0001). Most caregivers of both sexes considered that taking care of their relative did not pose a significant burden. Conclusions: This study provides a preliminary insight into sex-related characteristics in the management of advanced NSCLC and its impact on the emotional, social and economic burden of patients and their caregivers, and recall the high priority of researching in cancer from a sex perspective. Nevertheless, due to the low recruitment rate and the relevant loss of patients during the follow-up, it was difficult to find differences by sex. Trial registration: ClinicalTrials.gov identifier: NCT02336061. Ethics committee: Comité Ético de Investigación Clínica del Hospital Clínic de Barcelona, Spain. Reference number: HCB/2014/0705

Reconstruction and measurement of (100) MeV energy electromagnetic activity from π0 arrow γγ decays in the MicroBooNE LArTPC

We present results on the reconstruction of electromagnetic (EM) activity from photons produced in charged current νμ interactions with final state π0s. We employ a fully-automated reconstruction chain capable of identifying EM showers of (100) MeV energy, relying on a combination of traditional reconstruction techniques together with novel machine-learning approaches. These studies demonstrate good energy resolution, and good agreement between data and simulation, relying on the reconstructed invariant π0 mass and other photon distributions for validation. The reconstruction techniques developed are applied to a selection of νμ + Ar → μ + π0 + X candidate events to demonstrate the potential for calorimetric separation of photons from electrons and reconstruction of π0 kinematics

Calibration of the charge and energy loss per unit length of the MicroBooNE liquid argon time projection chamber using muons and protons

We describe a method used to calibrate the position- and time-dependent response of the MicroBooNE liquid argon time projection chamber anode wires to ionization particle energy loss. The method makes use of crossing cosmic-ray muons to partially correct anode wire signals for multiple effects as a function of time and position, including cross-connected TPC wires, space charge effects, electron attachment to impurities, diffusion, and recombination. The overall energy scale is then determined using fully-contained beam-induced muons originating and stopping in the active region of the detector. Using this method, we obtain an absolute energy scale uncertainty of 2% in data. We use stopping protons to further refine the relation between the measured charge and the energy loss for highly-ionizing particles. This data-driven detector calibration improves both the measurement of total deposited energy and particle identification based on energy loss per unit length as a function of residual range. As an example, the proton selection efficiency is increased by 2% after detector calibration