Studies of platinum electroplating baths Part VI: Influence of some experimental parameters on deposit quality

The influence of the conditions on the rate of Pt electroplating and the quality of the deposited layers are reported for the Pt 5Q bath (26mM Pt(NH3)4HPO4 +ap0mM sodium phosphate buffer adjusted to pH 10.4 with NaOH). It is confirmed that much higher rates of deposition can be achieved using constant potential control and the resulting deposits are less stressed than those formed by constant current plating. Deposition with high current efficiency is possible at 358K but again it is more readily achieved at constant potential; the rate of deposition is, however, lower and the plate quality is not as good as observed at 368K. Finally it is reported that three types of deposit can be identified by scanning electron microscopy (almost featureless, hemispherical growth centres leading to cauliflower deposits, and angular crystallites) depending on the cathode potential.     

Studies of platinum electroplating baths Part VI: Influence of some experimental parameters on deposit quality

The influence of the conditions on the rate of Pt electroplating and the quality of the deposited layers are reported for the Pt 5Q bath (26mM Pt(NH3)4HPO4 +ap0mM sodium phosphate buffer adjusted to pH 10.4 with NaOH). It is confirmed that much higher rates of deposition can be achieved using constant potential control and the resulting deposits are less stressed than those formed by constant current plating. Deposition with high current efficiency is possible at 358K but again it is more readily achieved at constant potential; the rate of deposition is, however, lower and the plate quality is not as good as observed at 368K. Finally it is reported that three types of deposit can be identified by scanning electron microscopy (almost featureless, hemispherical growth centres leading to cauliflower deposits, and angular crystallites) depending on the cathode potential.     

Conditional t-SNE: more informative t-SNE embeddings

Machine Learning - Dimensionality reduction and manifold learning methods such as t-distributed stochastic neighbor embedding (t-SNE) are frequently used to map high-dimensional data into a...     

Structural insights of mechanically induced aluminum-doped hydroxyapatite nanoparticles by Rietveld refinement

Aluminum doped hydroxyapatite(HA:Al~(3+)) nanopowders were successfully prepared via a simple and efficient one-pot mechanochemical route. The effects of dopant loading on phase compositions and structural features were assessed by Rietveld analysis. The XRD-Rietveld refinement revealed the stabilization of HA in hexagonal structure for all the samples. The sharpness and intensity of the apatite-derived XRD peaks decreased as the dopant content increased to 10% due to the increase in lattice imperfections and mechanically induced amorphization. The incorporation of Al3+into the HA lattice decreased the unit cell parameters. From the FTIR measurements, the representing bands of apatite were identified in all cases. The mechanosynthesized nanopowders consisted of nanospheroids with an average size of 44 ± 20 nm and therefore are promising for bone tissue regeneration.     

A statistical significance testing approach to mining the most informative set of patterns

Hypothesis testing using constrained null models can be used to compute the significance of data mining results given what is already known about the data. We study the novel problem of finding the smallest set of patterns that explains most about the data in terms of a global p value. The resulting set of patterns, such as frequent patterns or clusterings, is the smallest set that statistically explains the data. We show that the newly formulated problem is, in its general form, NP-hard and there exists no efficient algorithm with finite approximation ratio. However, we show that in a special case a solution can be computed efficiently with a provable approximation ratio. We find that a greedy algorithm gives good results on real data and that, using our approach, we can formulate and solve many known data-mining tasks. We demonstrate our method on several data mining tasks. We conclude that our framework is able to identify in various settings a small set of patterns that statistically explains the data and to formulate data mining problems in the terms of statistical significance.     

TaqMan probe real-time polymerase chain reaction assay for the quantification of canine DNA in chicken nugget

This paper describes a short-amplicon-based TaqMan probe quantitative real-time PCR (qPCR) assay for the quantitative detection of canine meat in chicken nuggets, which are very popular across the world, including Malaysia. The assay targeted a 100-bp fragment of canine cytb gene using a canine-specific primer and TaqMan probe. Specificity against 10 different animals and plants species demonstrated threshold cycles (Ct) of 16.13 ± 0.12 to 16.25 ± 0.23 for canine DNA and negative results for the others in a 40-cycle reaction. The assay was tested for the quantification of up to 0.01% canine meat in deliberately spiked chicken nuggets with 99.7% PCR efficiency and 0.995 correlation coefficient. The analysis of the actual and qPCR predicted values showed a high recovery rate (from 87% ± 28% to 112% ± 19%) with a linear regression close to unity (R² = 0.999). Finally, samples of three halal-branded commercial chicken nuggets collected from different Malaysian outlets were screened for canine meat, but no contamination was demonstrated.     

Artificial neural networks and naturally ventilated buildings

The author found that the results obtained were reliable and indicates that neural networks can be used as a predictor for investigating window opening configurations to study the effects on interior air motion. Further study is needed in the development of the database to cover wider architectural parameters and the implementation of new types of network is also needed, as well as the need to consider variation spatial coefficients more fully.     

Thermal degradation of diethanolamine at stripper condition for CO2 capture: Product types and reaction mechanisms

Amine-based absorption/stripping is one of the promising technology for CO₂ capture from natural and industrial gas streams. During the process, amines and CO₂ undergo irreversible reactions to produce undesired compounds, which cause corrosion, foaming, increased viscosity and breakdown of equipment, ultimately contributing to the economic loss and environmental pollution. In this study, the thermal degradation of aqueous diethanolamine in the presence and absence of dissolved CO₂ was investigated. The experiments were performed in stainless steel cylinders. The results show that thermal degradation in the absence of CO₂ was a slow process; triethanolamine, and tris(2-aminoethyl)amine were only the degradation products identified in the mixture In addition, the rate of degradation was very low, only 3% degradation was observed after 4 weeks. But in the presence of CO₂, sixteen degradation products were identified, nine of which were new degradation products reported for the first time in this study. The 3-(2-hydroxyethyl)-2-oxazolidinone, 1,4-bis(2-hydroxyethyl)piperazine and triethanolamine were the most abundant degradation products. The remaining DEA concentration after 4 weeks was about 20% of the total amine concentration. The most probable degradation reactions and their mechanisms are also proposed.     

Sustainable GQDs for potential application in engineering using corn powder as green precursor

Abstract

An alternative way to produce Graphene Quantum Dots (GQDs) economically is proposed in this study where corn powder was used as a green precursor to fabricate GQDs in a hassle less one-step route. These GQDs showed a stable, size-dependent in aqueous solution. The size of GQDs obtained from this work is approximately measure from 0.21 to 5.20?nm. The XRD patterns showed a broad peak at 22?Å. Furthermore, the UV-vis absorption spectrum has a broad peak around the range of 250–350?nm which is ascribed to the typical absorption of n-π* transition of the carbonyl groups. The said sample also exhibited bright green photoluminescence under an UV light. The Raman spectra displayed a D band around 1375?cm^?1 and G band around 1578?cm^?1. The performed analysis confirms that the GQDs by this method has a similar result compared to the other methods. This suggests that corn powder as a precursor can be used to fabricate GQDs in a simplistic and environment-friendly way for potential application in sensors, very large scale integration and energy generation technology.     

Artificial neural networks and naturally ventilated buildings

The author found that the results obtained were reliable and indicates that neural networks can be used as a predictor for investigating window opening configurations to study the effects on interior air motion. Further study is needed in the development of the database to cover wider architectural parameters and the implementation of new types of network is also needed, as well as the need to consider variation spatial coefficients more fully.