Ashwani Jha

Effect of interlamellar spacing on the mechanical properties of 0.65% C steel

The mechanical properties of a steel containing a nearly fully pearlitic structure have been examined as a function of the interlamellar spacing. The steel had been heat-treated at different austenitization temperatures in order to obtain varying interlamellar spacings. It was observed that hardness and yield strength follow a Hall–Petch type of relationship with respect to the interlamellar spacing but the ultimate tensile strength (UTS), percent elongation and impact toughness did not do so. It was noted that, below a critical size of interlamellar spacing, the UTS, impact toughness and ductility remained invariant to the interlamellar spacing. The results have been explained on the basis of a microstructure–thermal residual stress relationship.

Correlating microstructural features and mechanical properties with abrasion resistance of a high strength low alloy steel

A study towards the examination of the abrasive wear behaviour and other characteristics, viz. microstructure, tensile properties and hardness of a high strength low alloy steel has been carried out in order to establish a correlation amongst the parameters and to optimize the microstructural features and mechanical properties for superior wear performance. The steel was subjected to various heat treatment cycles for generating different combinations of microstructural features and mechanical and wear properties. The study suggests that, apart from hardness, ductility also plays a vital role in deciding the wear characteristics of steels. It has also been observed that an improvement in the abrasion resistance of the order of ∼50% can be achieved by subjecting the steel to suitable heat treatment cycles. Mechanical properties of the steel also change simultaneously. These features are ultimately controlled by the microstructural characteristics of the specimens. The results obtained have been supplemented through the characteristics of the worn surfaces, subsurface regions, debris and fractured surfaces. These analyses also helped to understand the operative mechanisms of material removal and failure.

Low-Stress Abrasive Wear Behaviour of a 0.2% C Steel: Influence of Microstructure and Test Parameters

A low (0.2%) carbon steel has been subjected to heat treatment to form varying quantities of ferrite plus martensite in its microstructure. This was achieved by holding the samples in the two-phase (ferrite plus austenite) region at three different temperatures (750, 780, and 810 °C) for a specific duration followed by quenching in ice water. In another exercise, the steel was also subjected to annealing treatment by austenitizing at 890 °C followed by furnace cooling for comparison purposes. The samples were subjected to low-stress (three-body) abrasion tests using an ASTM rubber wheel abrasion test apparatus at different wheel speeds (150, 273 and 400 rpm corresponding to linear speeds of 1.79, 3.26 and 4.78 m/s respectively) for different sliding distances at a fixed load of 49 N. Crushed silica sand particles of size ranging from 212 to 300 μm were used as the abrasive medium. The wear rate of samples decreased progressively with sliding distance until a (nearly) steady-state condition was attained. This was considered to be due to abrasion-induced work hardening of subsurface regions as well as the greater tendency of protrusion of the harder martensite/pearlite phase at longer sliding distances, thereby providing greater resistance to wear. Decreasing wear rate with increasing treatment temperature 750–810 °C could be attributed to the greater volume fraction of the hard martensite phase in the samples containing ferrite plus martensite. The lower wear rate observed in the case of the samples containing ferrite plus martensite over the annealed ones comprising ferrite and pearlite was attributed to the higher bulk hardness of the former. Increasing linear speed from 1.79 to 3.26 m/s led to an increase in wear rate. This could be attributed to greater tendency of the abrasive particles to create deeper scratches and scouping (digging). A reduction in wear rate with a further increase in the linear speed from 3.26 to 4.78 m/s could be due to a change in the mechanism of wear from predominantly sliding to rolling of the abrasive particles in view of the increased plastic deformability characteristics of the specimens due to higher frictional heating. The present investigation clearly suggests that it is possible to attain a desired combination of bulk hardness and microstructure (consisting of ferrite plus martensite) leading to optimum abrasion resistance in low-carbon steels. The quantity of the two phases in turn could be varied by suitably controlling the heat-treatment temperature.

Dry sliding wear behaviour of an aluminium alloy-granite particle composite

Abstract In the present study, the effect of granite reinforcement on the dry sliding wear behaviour of an aluminium–silicon alloy (BS:LM6) was investigated using a pin-on-disc machine. The composite was prepared using liquid metallurgy technique wherein 10 wt.% granite particles were incorporated in the matrix alloy. Sliding wear tests were conducted at applied loads in the range 0.2–1.6 MPa and speeds of 1.89, 3.96 and 5.55 m/s. The matrix alloy was also prepared and tested under identical conditions in order to see the influence of the dispersoid phase on wear behaviour. It was observed that the composite exhibited lower wear rate than that of the matrix alloy. Increasing applied load increased the wear rate. In the case of the composite, the wear rate decreased with speed except at higher pressures at the maximum speed; the trend reversed in the latter case. On the contrary, the matrix alloy exhibited minimum wear rate at the intermediate test speed. Seizure pressure of the composite was significantly higher than that of the matrix alloy, while temperature rise near the contacting surfaces and the coefficient of friction followed an opposite trend. SEM examination of the worn surfaces, subsurface regions and debris enabled to understand the operating wear mechanisms.

Two-body abrasive wear behaviour of aluminium alloy–sillimanite particle reinforced composite

In the present paper two-body abrasive wear behaviour of the cast aluminium alloy and aluminium alloy–10 wt.% sillimanite particle composite has been studied at different applied loads and abrasive sizes for different sliding distances. It was noted that the wear rate decreased with sliding distance and approached to a stable value and increased with increase in abrasive size and applied load irrespective of the materials. It was interesting to note that at 25 μm abrasive size, composite showed superior wear resistance to that of alloy but at 200 μm abrasive size, the former one suffered from inferior wear resistance than the later one irrespective of applied load. In the intermediate abrasive size (100 μm) the composite exhibited superior wear resistance than that of alloy at lower applied load, whereas at higher applied load the trend is reversed. These facts have been studied through wear surface, subsurface and wear debris analysis.

RNA-seq-mediated transcriptome analysis of actively growing and winter dormant shoots identifies non-deciduous habit of evergreen tree tea during winters

Tea [Camellia sinensis (L.) O. Kuntze] is a perennial tree which undergoes winter dormancy and unlike deciduous trees, the species does not shed its leaves during winters. The present work dissected the molecular processes operating in the leaves during the period of active growth and winter dormancy through transcriptome analysis to understand a long-standing question: why should tea be a non-deciduous species? Analyses of 24,700 unigenes obtained from 57,767 primarily assembled transcripts showed (i) operation of mechanisms of winter tolerance, (ii) down-regulation of genes involved in growth, development, protein synthesis and cell division, and (iii) inhibition of leaf abscission due to modulation of senescence related processes during winter dormancy in tea. These senescence related processes exhibited modulation to favour leaf abscission (i) in deciduous Populus tremula during winters, and (ii) also in tea but under osmotic stress during which leaves also abscise. These results validated the relevance of the identified senescence related processes for leaf abscission and suggested their operation when in need in tea.

Employing machine learning for reliable miRNA target identification in plants

BackgroundmiRNAs are ~21 nucleotide long small noncoding RNA molecules, formed endogenously in most of the eukaryotes, which mainly control their target genes post transcriptionally by interacting and silencing them. While a lot of tools has been developed for animal miRNA target system, plant miRNA target identification system has witnessed limited development. Most of them have been centered around exact complementarity match. Very few of them considered other factors like multiple target sites and role of flanking regions.ResultIn the present work, a Support Vector Regression (SVR) approach has been implemented for plant miRNA target identification, utilizing position specific dinucleotide density variation information around the target sites, to yield highly reliable result. It has been named as p-TAREF (plant-Target Refiner). Performance comparison for p-TAREF was done with other prediction tools for plants with utmost rigor and where p-TAREF was found better performing in several aspects. Further, p-TAREF was run over the experimentally validated miRNA targets from species like Arabidopsis, Medicago, Rice and Tomato, and detected them accurately, suggesting gross usability of p-TAREF for plant species. Using p-TAREF, target identification was done for the complete Rice transcriptome, supported by expression and degradome based data. miR156 was found as an important component of the Rice regulatory system, where control of genes associated with growth and transcription looked predominant. The entire methodology has been implemented in a multi-threaded parallel architecture in Java, to enable fast processing for web-server version as well as standalone version. This also makes it to run even on a simple desktop computer in concurrent mode. It also provides a facility to gather experimental support for predictions made, through on the spot expression data analysis, in its web-server version.ConclusionA machine learning multivariate feature tool has been implemented in parallel and locally installable form, for plant miRNA target identification. The performance was assessed and compared through comprehensive testing and benchmarking, suggesting a reliable performance and gross usability for transcriptome wide plant miRNA target identification.

miReader: Discovering Novel miRNAs in Species without Sequenced Genome

Along with computational approaches, NGS led technologies have caused a major impact upon the discoveries made in the area of miRNA biology, including novel miRNAs identification. However, to this date all microRNA discovery tools compulsorily depend upon the availability of reference or genomic sequences. Here, for the first time a novel approach, miReader, has been introduced which could discover novel miRNAs without any dependence upon genomic/reference sequences. The approach used NGS read data to build highly accurate miRNA models, molded through a Multi-boosting algorithm with Best-First Tree as its base classifier. It was comprehensively tested over large amount of experimental data from wide range of species including human, plants, nematode, zebrafish and fruit fly, performing consistently with >90% accuracy. Using the same tool over Illumina read data for Miscanthus, a plant whose genome is not sequenced; the study reported 21 novel mature miRNA duplex candidates. Considering the fact that miRNA discovery requires handling of high throughput data, the entire approach has been implemented in a standalone parallel architecture. This work is expected to cause a positive impact over the area of miRNA discovery in majority of species, where genomic sequence availability would not be a compulsion any more.

miR-BAG: Bagging Based Identification of MicroRNA Precursors

Non-coding elements such as miRNAs play key regulatory roles in living systems. These ultra-short, ∼21 bp long, RNA molecules are derived from their hairpin precursors and usually participate in negative gene regulation by binding the target mRNAs. Discovering miRNA candidate regions across the genome has been a challenging problem. Most of the existing tools work reliably only for limited datasets. Here, we have presented a novel reliable approach, miR-BAG, developed to identify miRNA candidate regions in genomes by scanning sequences as well as by using next generation sequencing (NGS) data. miR-BAG utilizes a bootstrap aggregation based machine learning approach, successfully creating an ensemble of complementary learners to attain high accuracy while balancing sensitivity and specificity. miR-BAG was developed for wide range of species and tested extensively for performance over a wide range of experimentally validated data. Consideration of position-specific variation of triplet structural profiles and mature miRNA anchored structural profiles had a positive impact on performance. miR-BAG’s performance was found consistent and the accuracy level was observed to be >90% for most of the species considered in the present study. In a detailed comparative analysis, miR-BAG performed better than six existing tools. Using miR-BAG NGS module, we identified a total of 22 novel miRNA candidate regions in cow genome in addition to a total of 42 cow specific miRNA regions. In practice, discovery of miRNA regions in a genome demands high-throughput data analysis, requiring large amount of processing. Considering this, miR-BAG has been developed in multi-threaded parallel architecture as a web server as well as a user friendly GUI standalone version.

Effects of some material and experimental variables on the slurry wear characteristics of zinc-aluminum alloys

In this study, the slurry wear behavior of a zinc-based alloy has been examined by the sample rotation method over a range of traversal speeds and distances. The influence of adding silicon to the alloy system on its wear characteristics has also been examined.The wear rate of the samples increased with increasing traversal distance initially, attained a peak, and then tended to decrease at longer distances. The initial increase in wear rate with distance was attributed to the indenting effect of the slurry constituents (i.e., liquid droplets and the erodant particles) associated with the corrosive action of the liquid in slurry. On the contrary, factors such as entrapment of the erodant mass as well as the corrosion products in the cavities formed on the specimen surfaces could lead to the decrease in wear rate at longer traversal distances. The existence of silicon particles in the alloy microstructure led to improved wear resistance of the alloy system. This was due to the resistance offered by the hard silicon particles against the impinging action of the slurry constituents. Attainment of the wear rate peak at longer traversal distances in the case of the silicon-containing alloy over the one without the element further substantiated the superior wear resistance offered by the silicon particles. Traversal speed led to higher wear rates irrespective of the test conditions and material composition due to the more severe attack of the medium on the specimen surface. However, the presence of silicon particles in the alloy microstructure offered improved wear resistance (inverse of wear rate).