Silver nanocluster formation in silicate glass by single step ion-exchange

We report the formation of silver nanoclusters of average size ∼ 1.1 nm in a single step ion-exchanged silica glass. Silver clusters are formed in a glass during ion-exchange by fine-tuning the ion-exchange parameters such as salt composition, temperature and time. Such nanocluster formation during ion-exchange preserves the waveguiding properties of the ion-exchanged surface of the glass making them suitable for nonlinear waveguide device fabrication.     

An experiment for showing some kind of artificial understanding

In this paper we design an experiment which can be depicted as a simple scenario, a very limited 'world'. In this world, there are an actor that can pursue a project and an observer that is keeping its eyes on the actor. We try to show in the experiment that the observer can to some degree understand the actor based on its knowledge and some metaphors, i.e. understand what the actor is doing and why. As the conclusion of this experiment, we try to show some features of 'understanding'. These are (1) that 'understanding' has to be based on some preliminary knowledge; (2) that 'understanding' is a process of incremental learning; (3) that, as for symbolic systems, some metaphors are necessary for mapping real entities into concepts in mind.     

Use of the time constant to detect corrosion speed in reinforced concrete structures

The relatively simple to measure ‘time constant’ is presented as an index for characterizing the level of corrosion experienced by steel in concrete. This communication, largely in the form of a technical note, explicitly illustrates that the time constant determined from electrochemical testing has significant merit for monitoring corrosion of steel in concrete, and appears to be insensitive to the area of electrode being probed. In this work, the time constant, κ, is determined following a galvanostatic excitation signal, revealing a good correlation between the value of κ and corrosion status across specimens of widely varying geometry. Although this notion has been suggested in the past, this work presents both a survey and consolidated review to indicate the utility of the parameter as an index to corrosion rate on-site.     

Experiments and simulations of the mechanical performance of repaired panels

Firstly, the compress experiment is undertaken to investigate the efficiency of repaired panels in this paper, and then modeling of the mechanical behavior of the repaired composite panel under compressive static load is conducted by using of the finite element method. The effect of geometric non-linearity on the stress–strain response is considered in the numeric analysis. Fatherly, the user material subroutine (UMAT) is integrated with the ABAQUS package with the geometric non-linearity effect for studying the damage initiation and its progression in the composite structure, and quadrilateral, linear, thick shell elements (S8R) are adopted. Finally, the predicted strain distribution, damage evolution and strength of the laminate are compared with the test results.     

Effect of Magnetotactic bacteria on the susceptibility in Chinese loess–paleosol sequences

A simulated experiment of Magnetotactic bacteria (MB) effect on susceptibility in the samples from the 5th to 6th loess layers (L₅–S₅–L₆) in Xifeng and Duangjiapo sections was performed. It is demonstrated that MB can increase the susceptibility of the samples and promote newly produced magnetic minerals from paleosol layers under the suitable condition. The tiny magnetic particles and arranged as a chain in the minerals less than 30 μm were found in the paleosol sample of Duangjiapo section.     

Machine-learning paradigms for selecting ecologically significant input variables

Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine-learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a (Chl-a) itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1–2 weeks.     

Lubricating behavior of iron rhenate and its relation to matching pairs

The iron rhenate was compounded by chemical method, which may be generated during wear process of Fe-Re alloys at elevated temperature. The friction coefficient of iron rhenate has been determined by a pin-on-disc device. The results show that iron rhenate plays role of lubricating at RT-600 °C. The lubricating behavior is related to the matching pairs. The hardness of the materials of matching pairs, compatibility of iron rhenate with materials of matching pairs and surface roughness of disc affect the lubricating behavior of iron rhenate mostly. The principle of matching pairs is proposed. Project supported by the National Natural Science Foundation of China Synopsis of the first author Xiong Dangsheng. associated professor, born in 1960. Current research fields include high-temperature self-lubricating alloys and tribological properties of material.     

Pd/Mg(OH)2 Heterogeneous Nanocatalysts Synthesized by a Facile One-Pot Hydrothermal Method for CO Direct Esterification to Dimethyl Oxalate

Catalysis Letters - Pd-based heterogeneous nanocatalysts have wide application in chemical industry. However, the traditional synthesis process contains multi-steps such as impregnation, dry,...     

Effect of preparation conditions on the properties of a coal-derived activated carbon honeycomb monolith

Activated carbon honeycomb (ACH) monoliths were prepared by extruding of a mixture of bituminous coal and organic additives and subsequent carbonization and steam activation. Preparation parameters that were varied were carbonization temperature and activation time. The carbonization conditions were 500, 650 and 800 °C for 1 h and the steam activation conditions were 850 °C for 2, 4 and 6 h. The monoliths at various states were characterized by SEM, XRD, nitrogen adsorption and compression test. It was found that carbonization temperature has significant effects on pore size distribution and mechanical strength of ACH monoliths. The ACH monoliths prepared from high carbonization temperatures exhibited lower values of the BET surface area and total pore volume and higher value of the mechanical strength than those of the ACH monoliths prepared from low carbonization temperatures. This was attributed to the effect of high temperature carbonization that results in the formation of relatively less defective structures.