Co-constructive development of a green chemistry-based model for the assessment of nanoparticles synthesis.

Nanomaterials (materials at the nanoscale, 10-9m) are extensively used in several industry sectors due to the improved properties they empower commercial products with. There is a pressing need to produce these materials more sustainably. This paper proposes a MCDA approach to assess the implementation of green chemistry principles as applied to the protocols for nanoparticles synthesis. In the presence of multiple green and environmentally oriented criteria, decision aiding is performed with a synergy of ordinal regression methods; preference information in the form of desired assignment for a subset of reference protocols is accepted. The classification models, indirectly derived from such information, are composed of an additive value function and a vector of thresholds separating the pre-defined and ordered classes. The method delivers a single representative model that is used to assess the relative importance of the criteria, identify the possible gains with improvement of the protocol's evaluations and classify the non-reference protocols. Such precise recommendation is validated against the outcomes of robustness analysis exploiting the sets of all classification models compatible with all maximal subsets of consistent assignment examples. The introduced approach is used with real-world data concerning silver nanoparticles. It is proven to effectively resolve inconsistency in the assignment examples, tolerate ordinal and cardinal measurement scales, differentiate between inter- and intra-criteria attractiveness and deliver easily interpretable scores and class assignments. This work thoroughly discusses the learning insights that MCDA provided during the co-constructive development of the classification model, distinguishing between problem structuring, preference elicitation, learning, modeling and problem-solving stages.

Phytoremediation-biorefinery tandem for effective clean-up of metal contaminated soil and biomass valorisation.

During the last few decades, phytoremediation process has attracted much attention because of the growing concerns about the deteriorating quality of soil caused by anthropogenic activities. Here, a tandem phytoremediation/biorefinery process was proposed as a way to turn phytoremediation into a viable commercial method by producing valuable chemicals in addition to cleaned soil. Two agricultural plants (Sinapis alba and Helianthus annuus) were grown in moderately contaminated soil with ca. 100 ppm of Ni and further degraded by a fungal lignin degrader-Phanerochaete chrysosporium. Several parameters have been studied, including the viability of plants, biomass yield, and their accumulating and remediating potentials. Further, downstream processing showed that up to 80% of Ni can be easily extracted from contaminated biomass by aqueous extraction at mild conditions. Finally, it was demonstrated that the growth of plants on the contaminated soil could be degraded by P. chrysosporium, and the effect of nickel and biomass pretreatment on the solid-state fermentation was studied. The proposed and studied methodology in this work could pave the way for successful commercialization of the phytoremediation process in the near future.

Robustness analysis of a green chemistry-based model for the classification of silver nanoparticles synthesis processes.

This paper proposes a robustness analysis based on Multiple Criteria Decision Aiding (MCDA). The ensuing model was used to assess the implementation of green chemistry principles in the synthesis of silver nanoparticles. Its recommendations were also compared to an earlier developed model for the same purpose to investigate concordance between the models and potential decision support synergies. A three-phase procedure was adopted to achieve the research objectives. Firstly, an ordinal ranking of the evaluation criteria used to characterize the implementation of green chemistry principles was identified through relative ranking analysis. Secondly, a structured selection process for an MCDA classification method was conducted, which ensued in the identification of Stochastic Multi-Criteria Acceptability Analysis (SMAA). Lastly, the agreement of the classifications by the two MCDA models and the resulting synergistic role of decision recommendations were studied. This comparison showed that the results of the two models agree between 76% and 93% of the simulation set-ups and it confirmed that different MCDA models provide a more inclusive and transparent set of recommendations. This integrative research confirmed the beneficial complementary use of MCDA methods to aid responsible development of nanosynthesis, by accounting for multiple objectives and helping communication of complex information in a comprehensive and traceable format, suitable for stakeholders and/or decision-makers with diverse backgrounds.

Interlayer Hybridization of Virgin Carbon, Recycled Carbon and Natural Fiber Laminates.

To meet sustainability objectives in the transport sector, natural fiber (NF) and recycled carbon fiber (RCF) have been developed, although they have been typically limited to low to medium performance components. This work has considered the effect of interlayer hybridization of woven NF and non-woven RCF with woven virgin carbon fibers (VCF) on the mechanical and damping performance of hybrid laminates, produced using double bag vacuum infusion (DBVI). The mean damping ratio of the pure laminates showed a trend of NF>RCF>VCF, which was inversely proportional to their modulus. The tensile, flexural and damping properties of hybrid laminates were dominated by the outermost ply. The VCF-RCF and VCF-NF hybrid laminates showed a comparatively greater mean damping ratio. The results of this work demonstrate a method for the uptake of alternative materials with a minimal impact on the mechanical properties and improved damping performance.

Improved hydrogen gas production in microbial electrolysis cells using inexpensive recycled carbon fibre fabrics.

Growing energy demands of wastewater treatment have made it vital for water companies to develop less energy intensive processes for treating wastewater if net zero emissions are to be achieved by 2050. Microbial electrolysis cells (MECs) have the potential to do this by treating water and producing renewable hydrogen gas as a product, but capital and operational costs have slowed their deployment. By using recycled carbon fibre mats, commercially viable MECs can brought closer to reality, where recycled carbon fibre anode MECs treating real wastewater (normalised ~3100 L d-1) were producing 66.77 L H2 d-1 while graphite felt anode MECs produced 3.65 L H2 d-1 per 1 m3 reactor, anodes costing £5.53 m-2 and £88.36 m-2 respectively, resulting in a total anode cost saving of 93%. This could incentivise the development of larger pilot systems, opening the door for generating greater value and a more sustainable wastewater treatment industry.

Does peak nasal inspiratory flow relate to peak expiratory flow?

AIMS: Measurement of Peak Nasal Inspiratory Flow (PNIF) seems to be a cheap and easily performed method to assess nasal patency. As demonstrated in a previous work, PNIF is influenced by SEX, AGE and HEIGHT. However there is a large degree of between-patient variability in PNIF levels. The purpose of this analysis is to determine whether the measurement of the pulmonary ventilatory capacity, by mean of Peak Expiratory Flow (PEF), enables more precise determination of PNIF. METHODS AND RESULTS: Repeated measurements of PNIF and PEF were performed in 112 volunteers. 100 of these fulfilled the study criteria (55 females and 45 males) and all of them were non-smokers, non-asthmatic, without nose and paranasal sinuses problems, with ages ranging from 15 to 71 years. Statistical analysis was undertaken to determine whether a relationship existed between PNIF and age, sex and height, but which also considered PEF. The data from both experiments were analysed together. In both groups there is a clear tendency for PNIF to increase with PEF. As clearly demonstrated in this work the value of PEF is informative in predicting PNIF and that the larger the value of PEF, the larger the value of PNIF. CONCLUSIONS: PNIF is a useful method to study nasal patency in both primary and secondary care to aid diagnosis of nasal disease, but low values of PNIF have to be confirmed by a study of the PEF as PNIF low values may be an expression of low ventilatory activity rather than an expression of nasal obstruction.

Peak nasal inspiratory flow; normal range in adult population.

AIMS: Measurement of Peak Nasal Inspiratory Flow (PNIF) seems to be a cheap, simple, easily performed method to assess nasal patency. The purpose of this study is to establish normative PNIF data for a healthy adult population and provide charts relating PNIF normal values with age, height and sex in adults. METHODS AND RESULTS: Repeated measurements of PNIF were performed in 170 volunteers. In total, 137 of these fulfilled the study criteria (66 females and 50 males) and all of them were non-smokers, non-asthmatic, without nose and paranasal sinuses problems, with ages ranging from 16 to 84 years. Data were statistically analysed and tables were produced relating PNIF to age, sex and height. There was no interaction of sex with age or height. There was considerable residual variability of PNIF between individuals not explained by any of the variables studied. CONCLUSIONS: We conclude that PNIF could be a useful method to study nasal patency in both primary and secondary care to aid diagnosis of nasal disease. The study provides normative data for a Caucasian population. Further variables need to be explored to predict expected PNIF values more accurately.

Synthetic mimicking of plant oils and comparison with naturally grown products in polyurethane synthesis.

The use of plant oils as industrial feedstocks can often be hampered by their lack of optimization towards a particular process, as well as their development being risky; growing suitable volumes of crops to test can take up to five years. To circumvent this, we aimed to discover a method that would mimic plant oil profiles in the laboratory, and show that they exhibited similar properties to the naturally grown plant oils in a given process. Using the synthesis of polyurethanes as an example, we have synthesized six different polymers and demonstrated that plant oils will produce polymers with similar physical properties to those oils mimicked in the laboratory. The use of this mimicking process can be extended to other types of polymers to obtain a method for predicting the properties of a given material based on the plant oil composition of a crop before it is grown in bulk.

Bayesian modelling of extreme surges on the UK east coast.

The catastrophic surge event of 1953 on the eastern UK and northern European coastlines led to widespread agreement on the necessity of a coordinated response to understand the risk of future oceanographic flood events and, so far as possible, to afford protection against such events. One element of this response was better use of historical data and scientific knowledge in assessing flood risk. The timing of the event also coincided roughly with the birth of extreme value theory as a statistical discipline for measuring risks of extreme events, and over the last 50 years, as techniques have been developed and refined, various attempts have been made to improve the precision of flood risk assessment around the UK coastline. In part, this article provides a review of such developments. Our broader aim, however, is to show how modern statistical modelling techniques, allied with the tools of extreme value theory and knowledge of sea-dynamic physics, can lead to further improvements in flood risk assessment. Our long-term goal is a coherent spatial model that exploits spatial smoothness in the surge process characteristics and we outline the details of such a model. The analysis of the present article, however, is restricted to a site-by-site analysis of high-tide surges. Nonetheless, we argue that the Bayesian methodology adopted for such analysis enables a risk-based interpretation of results that is most natural in this setting, and preferable to inferences that are available from more conventional analyses.