An efficient route to aqueous phase synthesis of nanocrystalline γ-Al2O3 with high porosity: From stable boehmite colloids to large pore mesoporous alumina

In this paper we emphasise the important role of Pluronic F127 on the porosity of mesoporous alumina prepared from boehmite colloids. By focusing on the F127/boehmite interactions we show how the concepts of interface science may help to predict and improve the textural characteristics of mesoporous alumina. By varying the synthetic parameters, in particular the copolymer content, we show that the porosity of c-Al2O3 can be enhanced by 400% and the average pore diameter can be expanded from 5 to 14 nm. These results are discussed in terms of interactions between the Pluronic F127 and boehmite colloids, and are correlated to the critical micelle concentration (CMC) of the copolymer. The textural characteristics of the mesoporous alumina can be further improved either by introducing hydrocarbons in the preformed boehmite/copolymer sols or by concentrating the sols. In comparison with as-synthesised alumina, those prepared with F127 showed improved thermal stability. Furthermore, boehmite/copolymer sols were stable for all surfactant concentrations investigated and can give high quality coatings suitable for catalytic applications

Preparation and aggregation-induced emission of new 1,3,5-triazine-2,4,6-tricarboxamide with liquid crystal properties

The combination of aggregation-induced emission (AIE) and liquid crystal properties generates solid-state efficient luminescent liquid crystal materials.Here in, we reported the synthesis of 1,3,5-triazine-2,4,6-tricarboxamide and utilized it as a supramolecular organic motif for the AIE-active liquid crystal material.The compound exhibits high-intensity emission maxima at 417 and 468 nm in the solid state with excitation at 254 nm, whereas it shows weak emission in the solution phase. Also, this compound behaves as liquid crystalline material and shows columnar hexagonal mesophase with endothermic peaks at 73.4oC, 185.6oC, and exothermic peaks were observed at 181.9oC and 66.1oC with focal conic fan shape texture.The thermal data showed that the compound is stable up to 200oC

Larnite powders and larnite/silica aerogel composites as effective agents for CO2 sequestration by carbonation

This paper presents the results of the carbonation reaction of two sample types: larnite (Ca2SiO4) powders and larnite/silica aerogel composites, the larnite acting as an active phase in a process of direct mineral carbonation. First, larnite powders were synthesized by the reaction of colloidal silica and calcium nitrate in the presence of ethylene glycol. Then, to synthesize the composites, the surface of the larnite powders was chemically modified with 3-aminopropyltriethoxysilane (APTES), and later this mixture was added to a silica sol previously prepared from tetraethylorthosilicate (TEOS). The resulting humid gel was dried in an autoclave under supercritical conditions for the ethanol. The textures and chemical compositions of the powders and composites were characterized.The carbonation reaction of both types of samples was evaluated by means of X-ray diffraction and thermogravimetric analysis. Both techniques confirm the high efficiency of the reaction at room temperature and atmospheric pressure. A complete transformation of the silicate into carbonate resulted after submitting the samples to a flow of pure CO2 for 15 min. This indicates that for this reaction time, 1 t of larnite could eliminate about 550 kg of CO2. The grain size, porosity, and specific surface area are the factors controlling the reaction.Ministerio de Medio Ambiente A266/2007/3-11.1Ministerio de Educación y Ciencia MAT2005-0158

Treatment of end-of-life concrete in an innovative heating-air classification system for circular cement-based products

A stronger commitment towards Green Building and circular economy, in response to environmental concerns and economic trends, is evident in modern industrial cement and concrete production processes. The critical demand for an overall reduction in the environmental impact of the construction sector can be met through the consumption of high-grade supplementary raw materials. Advanced solutions are under development in current research activities that will be capable of up-cycling larger quantities of valuable raw materials from the fine fractions of End-of-Life (EoL) concrete waste. New technology, in particular the Heating-Air classification System (HAS), simultaneously applies a combination of heating and separation processes within a fluidized bed-like chamber under controlled temperatures (±600 °C) and treatment times (25–40 s). In that process, moisture and contaminants are removed from the EoL fine concrete aggregates (0–4 mm), yielding improved fine fractions, and ultrafine recycled concrete particles (<0.125 mm), consisting mainly of hydrated cement, thereby adding value to finer EoL concrete fractions. In this study, two types of ultrafine recycled concrete (either siliceous or limestone EoL concrete waste) are treated in a pilot HAS technology for their conversion into Supplementary Cementitious Material (SCM). The physico-chemical effect of the ultrafine recycled concrete particles and their potential use as SCM in new cement-based products is assessed by employing substitutions of up to 10% of the conventional binder. The environmental viability of their use as SCM is then evaluated in a Life Cycle Assessment (LCA). The results demonstrated accelerated hydration kinetics of the mortars that incorporated these SCMs at early ages and higher mechanical strengths at all curing ages. Optimal substitutions were established at 5%. The results suggested that the overall environmental impact could be reduced by up to 5% when employing the ultrafine recycled concrete particles as SCM in circular cement-based products, reducing greenhouse gas emissions by as much as 41 kg CO2 eq./ton of cement (i.e. 80 million tons CO2 eq./year). Finally, the environmental impacts were reduced even further by running the HAS on biofuel rather than fossil fuel.The authors of the present paper, prepared in the framework ofthe Project VEEP "Cost-Effective Recycling of C&DW in High AddedValue Energy Efficient Prefabricated Concrete Components forMassive Retrofitting of our Built Environment", wish to acknowl-edge the European Commission for its support. This project hasreceived funding from the European Union’s Horizon 2020 researchand innovation programme under grant agreement No 723582.This paper reflects only the author’s view and the European Com-mission is not responsible for any use that may be made of theinformation it contains.The authors are also grateful to the Spanish Ministry of Science,Innovation and Universities (MICIU) and the European RegionalDevelopment Fund (FEDER) for funding this line of research(RTI2018-097074-B-C21)

Higher-Order Representations for Visual Recognition

In this thesis, we present a simple and effective architecture called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs generalize classical orderless texture-based image models such as bag-of-visual-words and Fisher vector representations. However, unlike prior work, they can be trained in an end-to-end manner. In the experiments, we demonstrate that these representations generalize well to novel domains by fine-tuning and achieve excellent results on fine-grained, texture and scene recognition tasks. The visualization of fine-tuned convolutional filters shows that the models are able to capture highly localized attributes. We present a texture synthesis framework that allows us to visualize the pre-images of fine-grained categories and the invariances that are captured by these models. In order to enhance the discriminative power of the B-CNN representations, we investigate normalization techniques for rescaling the importance of individual features during aggregation. Spectral normalization scales the spectrum of the covariance matrix obtained after bilinear pooling and offers a significant improvement. However, the computation involves singular value decomposition, which is not computationally efficient on modern GPUs. We present an iteration-based approximation of matrix square-root along with its gradients to speed up the computation and study its effect on fine-tuning deep neural networks. Another approach is democratic aggregation, which aims to equalize the contributions of individual feature vector into the final pooled image descriptor. This achieves a comparable improvement, and can be approximated in a low-dimensional embedding unlike the spectral normalization. Therefore, this approach is friendly to aggregating higher-dimensional features. We demonstrate that the two approaches are closely related, and we discuss their trade-off between performance and efficiency