Isochronal recovery behaviour on electromagnetic properties of polycrystalline nickel zinc ferrite (Ni0.5Zn0.5Fe2O4) prepared via mechanical alloying

A new approach through heat treatment has been attempted by establishing defects by the process of quenching towards electrical and magnetic properties in the nickel zinc ferrite (Ni0.5Zn0.5Fe2O4) sample. The measured property values in permeability and hysteresis characteristic gave their recovery behaviour in which the values, after quenching were recovered after undergoing the annealing. Interestingly, a different trend observed in the permittivity value whereas the value was increased after quenching and subsequently recovered after annealing. The mechanisms which produced the changes is believed to be involved by defects in the form of vacancies, interstitials, microcracks and dislocations created during quenching which gave rise to changes in the values of the complex permeability and permittivity components and hysteresis behaviour.

When outliers are different

ABSTRACT When does the presence of an outlier in some measured property indicate that the outlying object differs qualitatively, rather than quantitatively, from other members of its apparent class? Historical astronomical examples include the many types of supernovae and short versus long gamma-ray bursts. A qualitative difference implies that some parameter has a characteristic scale, and hence its distribution cannot be a power law (that can have no such scale). If the distribution is a power law, the objects differ only quantitatively. The applicability of a power law to an empirical distribution may be tested by comparing the most extreme member to its next-most extreme. The probability distribution of their ratio is calculated, and compared to data for stars, radio and X-ray sources, and the fluxes, fluences, and rotation measures of fast radio bursts (FRBs). It is found with high statistical significance that the giant outburst of soft gamma repeater SGR 1806-20 differed qualitatively from its lesser outbursts and FRB 200428 differed qualitatively from other FRBs (by location in the Galaxy), but that in some supernova remnant models of rotation measure FRB 121102 is not, statistically significantly, an outlier.

Good reporting practice for thermophysical and thermochemical property measurements (IUPAC Technical Report)

Scientific projects frequently involve measurements of thermophysical, thermochemical, and other related properties of chemical compounds and materials. These measured property data have significant potential value for the scientific community, but incomplete and inaccurate reporting often hampers their utilization. The present IUPAC Technical Report summarizes the needs of chemical engineers and researchers as consumers of these data and shows how publishing practices can improve information transfer. In the Report, general principles of Good Reporting Practice are developed together with examples illustrating typical cases of reporting issues. Adoption of these principles will improve the quality, reproducibility, and usefulness of experimental data, bring a better level of consistency to results, and increase the efficiency and impact of research. Closely related to Good Reporting Practice, basic elements of Good Research Practice are also introduced with a goal to reduce the number of ambiguities and unresolved problems within the thermophysical property data domain.

Neural Network Based Adaptation Algorithm for Online Prediction of Mechanical Properties of Steel

After production of a steel product in a steel plant, a sample of the product is tested in a laboratory for its mechanical properties like yield strength (YS), ultimate tensile strength (UTS) and percentage elongation. This paper describes a mathematical model based method which can predict the mechanical properties without testing. A neural network based adaptation algorithm was developed to reduce the prediction error. The uniqueness of this adaptation algorithm is that the model trains itself very fast when predicted and measured data are incorporated to the model. Based on the algorithm, an ASP.Net based intranet website has also been developed for calculation of the mechanical properties. In the starting Furnace Module webpage, austenite grain size is calculated using semi-empirical equations of austenite grain size during heating of slab in a reheating furnace. In the Mill Module webpage, different conditions of static, dynamic and metadynamic recrystallization are calculated. In this module, austenite grain size is calculated from the recrystallization conditions using corresponding recrystallization and grain growth equations. The last module is a cooling module. In this module, the phase transformation equations are used to predict the grain size of ferrite phase. In this module, structure-property correlation is used to predict the final mechanical properties. In the Training Module, the neural network based adapation algorithm trains the model and stores the weights and bias in a database for future predictions. Finally, the model was trained and validated with measured property data.

Synthesis of Colloidal Au Nanoparticles through Ultrasonic Spray Pyrolysis and Their Use in the Preparation of Polyacrylate-AuNPs’ Composites

Colloidal gold nanoparticles (AuNPs) were prepared from two different liquid precursors (gold (III) acetate and gold (III) chloride), using the Ultrasonic Spray Pyrolysis (USP) process. The STEM characterisation showed that the AuNPs from gold chloride are spherical, with average diameters of 57.2 and 69.4 nm, while the AuNPs from gold acetate are ellipsoidal, with average diameters of 84.2 and 134.3 nm, according to Dynamic Light Scattering (DLS) measurements. UV/VIS spectroscopy revealed the maximum absorbance band of AuNPs between 532 and 560 nm, which indicates a stable state. Colloidal AuNPs were used as starting material and were mixed together with acrylic acid (AA) and acrylamide (Am) for the free radical polymerization of polyacrylate-AuNPs’ composites, with the purpose of using them for temporary cavity fillings in the dental industry. SEM characterisation of polyacrylate-AuNPs’ composites revealed a uniform distribution of AuNPs through the polymer matrix, revealing that the AuNPs remained stable during the polymerization process. The density measurements revealed that colloidal AuNPs increase the densities of the prepared polyacrylate-AuNPs’ composites; the densities were increased up to 40% in comparison with the densities of the control samples. A compressive test showed that polyacrylate-AuNPs’ composites exhibited lower compressive strength compared to the control samples, while their toughness increased. At 50% compression deformation some of the samples fracture, suggesting that incorporation of colloidal AuNPs do not improve their compressive strength, but increase their toughness significantly. This increased toughness is the measured property which makes prepared polyacrylate-AuNPs potentially useful in dentistry.

Relationships between wood properties of small clear specimens and structural-sized boards in three softwood species

Research on the mechanical and physical properties of wood is commonly carried out on either small clear specimens or structural-sized boards. The first approach was more frequently utilized in the past, while the latter is more commonly used nowadays. However, there is very little information on how the two approaches relate with one another. This study aimed to quantify the relationships between the mechanical [modulus of elasticity (MOE) and bending strength] and physical properties (density) of both specimen sizes. A total of 1376 structural-sized boards from three different species (Douglas-fir, Norway spruce and Sitka spruce) were tested in bending, after which a small clear specimen was extracted from the undamaged portion of each board and re-tested in bending. Prior to destructive testing, all boards and clear specimens were evaluated using non-destructive technology. Poor-to-moderate relationships were found between all measured mechanical and physical properties of structural-sized timber and small clear specimens. In both specimen sizes, the properties correlated with one another within the same specimen size, as well as across the two sizes. The strength of correlations appears to be somewhat species dependent. Relatively good relationships were identified when comparing the mean tree values of the properties examined, suggesting either method can be used for a tree-level comparison. The non-destructive evaluation of specimens was shown to reflect the measured properties moderately well, with the relationships changing significantly depending on which measured property was being predicted.

Equivalent alkane carbon number of crude oils: A predictive model based on machine learning

In this work, we present the development of models for the prediction of the Equivalent Alkane Carbon Number of a dead oil (EACNdo) usable in the context of Enhanced Oil Recovery (EOR) processes. Models were constructed by means of data mining tools. To that end, we collected 29 crude oil samples originating from around the world. Each of these crude oils have been experimentally analysed, and we measured property such as EACNdo, American Petroleum Institute (API) gravity and $ {\mathrm{C}}_{{20}^{-}}$ , saturate, aromatic, resin, and asphaltene fractions. All this information was put in form of a database. Evolutionary Algorithms (EA) have been applied to the database to derive models able to predict Equivalent Alkane Carbon Number (EACN) of a crude oil. Developed correlations returned EACNdo values in agreement with reference experimental data. Models have been used to feed a thermodynamics based models able to estimate the EACN of a live oil. The application of such strategy to study cases have demonstrated that combining these two models appears as a relevant tool for fast and accurate estimates of live crude oil EACNs.

Laboratory sample stability. Is it possible to define a consensus stability function? An example of five blood magnitudes

Background: The stability limit of an analyte in a biological sample can be defined as the time required until a measured property acquires a bias higher than a defined specification. Many studies assessing stability and presenting recommendations of stability limits are available, but differences among them are frequent. The aim of this study was to classify and to grade a set of bibliographic studies on the stability of five common blood measurands and subsequently generate a consensus stability function. Methods: First, a bibliographic search was made for stability studies for five analytes in blood: alanine aminotransferase (ALT), glucose, phosphorus, potassium and prostate specific antigen (PSA). The quality of every study was evaluated using an in-house grading tool. Second, the different conditions of stability were uniformly defined and the percent deviation (PD%) over time for each analyte and condition were scattered while unifying studies with similar conditions. Results: From the 37 articles considered as valid, up to 130 experiments were evaluated and 629 PD% data were included (106 for ALT, 180 for glucose, 113 for phosphorus, 145 for potassium and 85 for PSA). Consensus stability equations were established for glucose, potassium, phosphorus and PSA, but not for ALT. Conclusions: Time is the main variable affecting stability in medical laboratory samples. Bibliographic studies differ in recommedations of stability limits mainly because of different specifications for maximum allowable error. Definition of a consensus stability function in specific conditions can help laboratories define stability limits using their own quality specifications.

Applying Rate Transient Analysis (RTA) to integrate geological and engineering data

The Blasingame typecurve in Fekete’s Rate Transient Analysis (RTA) software has been used at Santos to increase the understanding and integration of well and reservoir data; however, the authors have discovered that in some cases the tool produced anomalous results, such as permeability being too low. The potential consequence of this was incorrectly writing off reserves or making projects (in particular compression projects) fail economic tests. After testing various hypotheses, a simple yet unorthodox solution was only discovered in a field where the anomaly was more profound, and required integrating geology and geophysics to explain it. This solution has since been applied in RTA models across numerous other fields, and it has improved the quality and confidence of these models. The solution was the realisation that in many cases the accessed gas in place (GIP) increased over time, but the underlying model in RTA assumes a single tank, linear P/z. Matching the RTA model with the initial reservoir pressure and final accessed GIP results in over-predicting the reservoir pressures, resulting in an artificially low permeability. The authors discovered that the appropriate well and reservoir parameters could be obtained by matching the late time data using a lower initial reservoir pressure value corresponding to when the well had accessed the final GIP volume but not the initial reservoir pressure. This step was initially regarded to be counter-intuitive as the initial pressure is a measured property. Numerous reviews have endorsed this methodology, which is now being used as a standard at Santos.