Sustainable CO2 adsorbents prepared by coating chitosan onto mesoporous silicas for large-scale carbon capture technology

In this article, we report a new sustainable synthesis procedure for manufacturing chitosan/silica CO2 adsorbents. Chitosan is a naturally abundant material and contains amine functionality, which is essential for selective CO2 adsorptions. It is, therefore, ideally suited for manufacturing CO2 adsorbents on a large scale. By coating chitosan onto high-surface-area mesoporous silica supports, including commercial fumed silica (an economical and accessible reagent) and synthetic SBA-15 and MCF silicas, we have prepared a new family of CO2 adsorbents, which have been fully characterised with nitrogen adsorption isotherms, thermogravimetric analysis/differential scanning calorimetry, TEM, FTIR spectroscopy and Raman spectroscopy. These adsorbents have achieved a significant CO2 adsorption capacity of up to 0.98 mmol g−1 at ambient conditions (P=1 atm and T=25 °C). The materials can also be fully regenerated/recycled on demand at temperatures as low as 75 °C with a >85 % retention of the adsorption capacity after 4 cycles, which makes them promising candidates for advanced CO2 capture, storage and utilisation technology

The synergistic effects of alloying on the performance and stability of Co3Mo and Co7Mo6 for the electrocatalytic hydrogen evolution reaction

Metal alloys have become a ubiquitous choice as catalysts for electrochemical hydrogen evolution in alkaline media. However, scarce and expensive Pt remains the key electrocatalyst in acidic electrolytes, making the search for earth-abundant and cheaper alternatives important. Herein, we present a facile and efficient synthetic route towards polycrystalline Co3Mo and Co7Mo6 alloys. The single-phased nature of the alloys is confirmed by X-ray diffraction and electron microscopy. When electrochemically tested, they achieve competitively low overpotentials of 115 mV (Co3Mo) and 160 mV (Co7Mo6) at 10 mA cm−2 in 0.5 M H2SO4, and 120 mV (Co3Mo) and 160 mV (Co7Mo6) at 10 mA cm−2 in 1 M KOH. Both alloys outperform Co and Mo metals, which showed significantly higher overpotentials and lower current densities when tested under identical conditions, confirming the synergistic effect of the alloying. However, the low overpotential in Co3Mo comes at the price of stability. It rapidly becomes inactive when tested under applied potential bias. On the other hand, Co7Mo6 retains the current density over time without evidence of current decay. The findings demonstrate that even in free-standing form and without nanostructuring, polycrystalline bimetallic electrocatalysts could challenge the dominance of Pt in acidic media if ways for improving their stability were found

Raman response of Stage-1 graphite intercalation compounds revisited

We present a detailed in-situ Raman analysis of stage-1 KC8, CaC6, and LiC6 graphite intercalation compounds (GIC) to unravel their intrinsic finger print. Four main components were found between 1200 cm-1 and 1700 cm-1, and each of them were assigned to a corresponding vibrational mode. From a detailed line shape analysis of the intrinsic Fano-lines of the G- and D-line response we precisely determine the position ({\omega}ph), line width ({\Gamma}ph) and asymmetry (q) from each component. The comparison to the theoretical calculated line width and position of each component allow us to extract the electron-phonon coupling constant of these compounds. A coupling constant {\lambda}ph < 0.06 was obtained. This highlights that Raman active modes alone are not sufficient to explain the superconductivity within the electron-phonon coupling mechanism in CaC6 and KC8.Comment: 6 pages, 3 figures, 2 table

Zero-Shot Deep Domain Adaptation

Domain adaptation is an important tool to transfer knowledge about a task (e.g. classification) learned in a source domain to a second, or target domain. Current approaches assume that task-relevant target-domain data is available during training. We demonstrate how to perform domain adaptation when no such task-relevant target-domain data is available. To tackle this issue, we propose zero-shot deep domain adaptation (ZDDA), which uses privileged information from task-irrelevant dual-domain pairs. ZDDA learns a source-domain representation which is not only tailored for the task of interest but also close to the target-domain representation. Therefore, the source-domain task of interest solution (e.g. a classifier for classification tasks) which is jointly trained with the source-domain representation can be applicable to both the source and target representations. Using the MNIST, Fashion-MNIST, NIST, EMNIST, and SUN RGB-D datasets, we show that ZDDA can perform domain adaptation in classification tasks without access to task-relevant target-domain training data. We also extend ZDDA to perform sensor fusion in the SUN RGB-D scene classification task by simulating task-relevant target-domain representations with task-relevant source-domain data. To the best of our knowledge, ZDDA is the first domain adaptation and sensor fusion method which requires no task-relevant target-domain data. The underlying principle is not particular to computer vision data, but should be extensible to other domains.Comment: This paper is accepted to the European Conference on Computer Vision (ECCV), 201

Object Contour and Edge Detection with RefineContourNet

A ResNet-based multi-path refinement CNN is used for object contour detection. For this task, we prioritise the effective utilization of the high-level abstraction capability of a ResNet, which leads to state-of-the-art results for edge detection. Keeping our focus in mind, we fuse the high, mid and low-level features in that specific order, which differs from many other approaches. It uses the tensor with the highest-levelled features as the starting point to combine it layer-by-layer with features of a lower abstraction level until it reaches the lowest level. We train this network on a modified PASCAL VOC 2012 dataset for object contour detection and evaluate on a refined PASCAL-val dataset reaching an excellent performance and an Optimal Dataset Scale (ODS) of 0.752. Furthermore, by fine-training on the BSDS500 dataset we reach state-of-the-art results for edge-detection with an ODS of 0.824.Comment: Keywords: Object Contour Detection, Edge Detection, Multi-Path Refinement CN

Spin frustration and magnetic ordering in theS=12molecular antiferromagnetfcc−Cs3C60

We have investigated the low-temperature magnetic state of face-centered-cubic (fcc) Cs3C60, a Mott insulator and the first molecular analog of a geometrically frustrated Heisenberg fcc antiferromagnet with S=1/2 spins. Specific heat studies reveal the presence of both long-range antiferromagnetic ordering and a magnetically disordered state below TN=2.2 K, which is in agreement with local probe experiments. These results together with the strongly suppressed TN are unexpected for conventional atom-based fcc antiferromagnets, implying that the fulleride molecular degrees of freedom give rise to the unique magnetic ground state

Class reconstruction driven adversarial domain adaptation for hyperspectral image classification

We address the problem of cross-domain classification of hyperspectral image (HSI) pairs under the notion of unsupervised domain adaptation (UDA). The UDA problem aims at classifying the test samples of a target domain by exploiting the labeled training samples from a related but different source domain. In this respect, the use of adversarial training driven domain classifiers is popular which seeks to learn a shared feature space for both the domains. However, such a formalism apparently fails to ensure the (i) discriminativeness, and (ii) non-redundancy of the learned space. In general, the feature space learned by domain classifier does not convey any meaningful insight regarding the data. On the other hand, we are interested in constraining the space which is deemed to be simultaneously discriminative and reconstructive at the class-scale. In particular, the reconstructive constraint enables the learning of category-specific meaningful feature abstractions and UDA in such a latent space is expected to better associate the domains. On the other hand, we consider an orthogonality constraint to ensure non-redundancy of the learned space. Experimental results obtained on benchmark HSI datasets (Botswana and Pavia) confirm the efficacy of the proposal approach

Deep semi-supervised segmentation with weight-averaged consistency targets

Recently proposed techniques for semi-supervised learning such as Temporal Ensembling and Mean Teacher have achieved state-of-the-art results in many important classification benchmarks. In this work, we expand the Mean Teacher approach to segmentation tasks and show that it can bring important improvements in a realistic small data regime using a publicly available multi-center dataset from the Magnetic Resonance Imaging (MRI) domain. We also devise a method to solve the problems that arise when using traditional data augmentation strategies for segmentation tasks on our new training scheme.Comment: 8 pages, 1 figure, accepted for DLMIA/MICCA

Two-electronic component behavior in the multiband FeSe0.42_{0.42}Te0.58_{0.58} superconductor

We report X-band EPR and $^{125}$Te and $^{77}$Se NMR measurements on single-crystalline superconducting FeSe$_{0.42}$Te$_{0.58}$ ($T_c$ = 11.5(1) K). The data provide evidence for the coexistence of intrinsic localized and itinerant electronic states. In the normal state, localized moments couple to itinerant electrons in the Fe(Se,Te) layers and affect the local spin susceptibility and spin fluctuations. Below $T_c$, spin fluctuations become rapidly suppressed and an unconventional superconducting state emerges in which $1/T_1$ is reduced at a much faster rate than expected for conventional $s$- or $s_\pm$-wave symmetry. We suggest that the localized states arise from the strong electronic correlations within one of the Fe-derived bands. The multiband electronic structure together with the electronic correlations thus determine the normal and superconducting states of the FeSe$_{1-x}$Te$_x$ family, which appears much closer to other high-$T_c$ superconductors than previously anticipated.Comment: 5 pages, 4 figure

Dynamic Adaptation on Non-Stationary Visual Domains

Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target. Prior work has examined domain adaptation in the context of stationary domain shifts, i.e. static data sets. However, with large-scale or dynamic data sources, data from a defined domain is not usually available all at once. For instance, in a streaming data scenario, dataset statistics effectively become a function of time. We introduce a framework for adaptation over non-stationary distribution shifts applicable to large-scale and streaming data scenarios. The model is adapted sequentially over incoming unsupervised streaming data batches. This enables improvements over several batches without the need for any additionally annotated data. To demonstrate the effectiveness of our proposed framework, we modify associative domain adaptation to work well on source and target data batches with unequal class distributions. We apply our method to several adaptation benchmark datasets for classification and show improved classifier accuracy not only for the currently adapted batch, but also when applied on future stream batches. Furthermore, we show the applicability of our associative learning modifications to semantic segmentation, where we achieve competitive results