Mesopore etching under supercritical conditions – A shortcut to hierarchically porous silica monoliths

Hierarchically porous silica monoliths are obtained in the two-step Nakanishi process, where formation of a macro microporous silica gel is followed by widening micropores to mesopores through surface etching. The latter step is carried out through hydrothermal treatment of the gel in alkaline solution and necessitates a lengthy solvent exchange of the aqueous pore fluid before the ripened gel can be dried and calcined into a mechanically stable macro mesoporous monolith. We show that using an ethanol water (95.6/4.4, v/v) azeotrope as supercritical fluid for mesopore etching eliminates the solvent exchange, ripening, and drying steps of the classic route and delivers silica monoliths that can withstand fast heating rates for calcination. The proposed shortcut decreases the overall preparation time from ca. one week to ca. one day. Porosity data show that the alkaline conditions for mesopore etching are crucial to obtain crack-free samples with a narrow mesopore size distribution. Physical reconstruction of selected samples by confocal laser scanning microscopy and subsequent morphological analysis confirms that monoliths prepared via the proposed shortcut possess the high homogeneity of silica skeleton and macropore space that is desirable in adsorbents for flow-through applications

Accelerating Neural Network Training with Distributed Asynchronous and Selective Optimization (DASO)

With increasing data and model complexities, the time required to train neural networks has become prohibitively large. To address the exponential rise in training time, users are turning to data parallel neural networks (DPNN) and large-scale distributed resources on computer clusters. Current DPNN approaches implement the network parameter updates by synchronizing and averaging gradients across all processes with blocking communication operations after each forward-backward pass. This synchronization is the central algorithmic bottleneck. We introduce the Distributed Asynchronous and Selective Optimization (DASO) method, which leverages multi-GPU compute node architectures to accelerate network training while maintaining accuracy. DASO uses a hierarchical and asynchronous communication scheme comprised of node-local and global networks while adjusting the global synchronization rate during the learning process. We show that DASO yields a reduction in training time of up to 34% on classical and state-of-the-art networks, as compared to current optimized data parallel training methods

Accelerating Neural Network Training with Distributed Asynchronous and Selective Optimization (DASO)

With increasing data and model complexities, the time required to train neural networks has become prohibitively large. To address the exponential rise in training time, users are turning to data parallel neural networks (DPNN) to utilize large-scale distributed resources on computer clusters. Current DPNN approaches implement the network parameter updates by synchronizing and averaging gradients across all processes with blocking communication operations. This synchronization is the central algorithmic bottleneck. To combat this, we introduce the Distributed Asynchronous and Selective Optimization (DASO) method which leverages multi-GPU compute node architectures to accelerate network training. DASO uses a hierarchical and asynchronous communication scheme comprised of node-local and global networks while adjusting the global synchronization rate during the learning process. We show that DASO yields a reduction in training time of up to 34% on classical and state-of-the-art networks, as compared to other existing data parallel training methods

Direct synthesis of carbide-derived carbon monoliths with hierarchical pore design by hard-templating

Carbide-derived carbon Monoliths (CDC-Ms) containing a multimodal arrangement with high volumes of micro- meso- and macropores are prepared by direct nanocasting of silica monoliths with polycarbosilane precursors. CDC-Ms show well-defined pore structures along with specific surface areas of more than 2600 m2 g−1 and overall pore volumes as high as 3.14 cm3 g−1. They exhibit advanced gas filtration properties compared to purely microporous materials due to enhanced storage capacities and kinetics as demonstrated by thermal response measurements based on InfraSORP technology

Direct synthesis of carbide-derived carbon monoliths with hierarchical pore design by hard-templating

Carbide-derived carbon Monoliths (CDC-Ms) containing a multimodal arrangement with high volumes of micro- meso- and macropores are prepared by direct nanocasting of silica monoliths with polycarbosilane precursors. CDC-Ms show well-defined pore structures along with specific surface areas of more than 2600 m2 g−1 and overall pore volumes as high as 3.14 cm3 g−1. They exhibit advanced gas filtration properties compared to purely microporous materials due to enhanced storage capacities and kinetics as demonstrated by thermal response measurements based on InfraSORP technology

Proton Conduction in Sulfonated Organic–Inorganic Hybrid Monoliths with Hierarchical Pore Structure

Porous organic–inorganic hybrid monoliths with hierarchical porosity exhibiting macro- and mesopores are prepared via sol–gel process under variation of the mesopore size. Organic moieties in the pore walls are incorporated by substituting up to 10% of the silicon precursor tetramethylorthosilicate with bisilylated benzene molecules. After functionalization with sulfonic acid groups, the resulting sulfonated hybrid monoliths featuring a bimodal pore structure are investigated regarding proton conduction depending on temperature and relative humidity. The hierarchical pore system and controlled mesopore design turn out to be crucial for sulfonation and proton conduction. These sulfonated hybrid hierarchical monoliths containing only 10% organic precursor exhibit higher proton conduction at different relative humidities than sulfonated periodic mesoporous organosilica made of 100% bisilylated precursors exhibiting solely mesopores, even with a lower concentration of sulfonic acid groups

Hierarchical Carbon with High Nitrogen Doping Level: A Versatile Anode and Cathode Host Material for Long-Life Lithium-Ion and Lithium–Sulfur Batteries

Nitrogen-rich carbon with both a turbostratic microstructure and meso/macroporosity was prepared by hard templating through pyrolysis of a tricyanomethanide-based ionic liquid in the voids of a silica monolith template. This multifunctional carbon not only is a promising anode candidate for long-life lithium-ion batteries but also shows favorable properties as anode and cathode host material owing to a high nitrogen content (>8% after carbonization at 900 °C). To demonstrate the latter, the hierarchical carbon was melt-infiltrated with sulfur as well as coated by atomic layer deposition (ALD) of anatase TiO₂, both of which led to high-quality nanocomposites. TiO₂ ALD increased the specific capacity of the carbon while maintaining high Coulombic efficiency and cycle life: the composite exhibited stable performance in lithium half-cells, with excellent recovery of low rate capacities after thousands of cycles at 5C. Lithium–sulfur batteries using the sulfur/carbon composite also showed good cyclability, with reversible capacities of ∼700 mA·h·g^–1 at C/5 and without obvious decay over several hundred cycles. The present results demonstrate that nitrogen-rich carbon with an interconnected multimodal pore structure is very versatile and can be used as both active and inactive electrode material in high-performance lithium-based batteries

helmholtz-analytics/heat: Heat 1.0: Data Parallel Neural Networks, and more

Release Notes Heat v1.0 comes with some major updates: new module nn for data-parallel neural networks Distributed Asynchronous and Selective Optimization (DASO) to accelerate network training on multi-GPU architectures support for complex numbers major documentation overhaul support channel on StackOverflow support PyTorch 1.8 stop supporting Python 3.6 many more updates and bug fixes, check out the CHANGELO