The Combustion Method for the preparation of the Hexa-ferrite

By -

 The Combustion Method


Why do we select the combustion method for the preparation of Hexa-ferrite?

              The advantage of the combustion method for the preparation of the hexaferrite is that we get the precursor material in a quick and easy manner, without any special setup or complicated chemical reaction, in contrast with some of the other methods used for the preparation of hexaferrite.  


Combustion Method:-


The mixture containing the solution of salts, ammonia, and citric acid at pH 7 was evaporated to dryness on a hot plate, at which point a self-propagating decomposition occurred. A violent exothermic reaction ignited and a foamy mass swelled up that propagated through the entire sample within a few seconds after which CO2 evolved and citric acid got polymerized and the cations got converted completely to alpha-Fe2O3 and BaCO3. The violent exothermic reaction that occurred first is the combustion of the NH4NO3 formed in the neutralization of the solution, and it produced a reaction temperature of 227*C. The maintenance of the homogeneity of the gas was ensured by the speed of the reaction and the evolution of gas produced a very porous foam structure that yielded a loose agglomeration (a mass or collection of things) when powdered. After heating the sample at 700*C the sample was mostly BaM with some a-Fe2O3 present in it, and it had formed pure BaM at 1000 C, but the problem is that the sample preparation has poor magnetic properties may be because of the small grain size resulting in poor magnetic ordering. The process of citrate-combustion can proceed either via direct combustion of an aqueous gel containing sufficient levels of oxidizer (Aqueous Combustion Synthesis, ACS) or the ignition of a dried citrate gel powder (Low-temperature Combustion Synthesis, LCS). Citrate: metal ions ratio was required to be around 0.68 to enable the wet gel to ignite at 200*C for the Aqueous Combustion Synthesis method, the gel ratio 1:1 required to dried for 18 hours at 120*C for the combustion to occur in the Low-temperature Combustion Synthesis method. The reason behind it is that because higher levels of citric acid absorbed more water via hydrogen bonding, inhibiting the reaction. Tetraformaltrisazine (TFTA, C4H12N6) and Oxalic acid dihydrate (ODH, C2H6N4O2) are other reducing agents that have also been used, which spontaneously ignite on dehydration and produce higher temperatures over 300*C, to yield BaM with a grain size of 0.2 lm and good magnetic properties at 850*C. To induce combustion, microwave heating can be used as well, heat is generated by the interaction of the microwaves with the material usually at 2.45 GHz, the frequency at which domestic microwave ovens operate. BaM nanopowders of single-phase were obtained by stirring a stoichiometric mixture of nitrates with citric acid in ethylenediaminetetraacetic acid and ethylene glycol, it was adjusted to pH 6.5 with ammonia solution and to oxidize all the chelating hydrocarbons it was required excess amount of ammonium nitrate also adjust with it. Now the mixture was freeze-dried and in a microwave oven, the mixture was auto combusted in a specially designed quartz vessel and sintered ferrite support to maximize the heating conditions. Single-phase BaM nanopowder is easily formed without any complication with the help of the auto combustion process.

Comments

Post a Comment

Please do not use foul language while commenting. We will appreciate your suggestions and reviews in the comments.

Read Also

Assisting Neuroimaging through DL

By -
Image
Introduction: In recent years, neuroimaging has been a research attraction, especially due to fMRI (Functional Magnetic Resonance Imaging) advancements. The fMRI data comprises of often several individuals. This data is used by researchers to study the association between the cognitive states of an individual and the underlying brain activities. The fMRI data used for neuroimaging is ideally suited for deep learning applications. The large, structured datasets of fMRI can be used for representation-learning methods of the DL. Generally, DL can be defined as a learning method with multiple levels of abstraction, where at each level the input data is transformed by a simple non-linear function which enables the model to recognize complex patterns. With higher-level representation, DL methods can associate a target variable with variable patterns in input data. Also, DL techniques can independently acquire these transformations from the data, eliminating the need for a comprehensive preex
Read more

Marine eDNA Analysis using DL techniques

By -
Image
Introduction Environmental DNA (eDNA) is the genetic information or material released by an organism into its surroundings through activities like skin shedding, mucus, urine or other secretions. These materials contain DNA fragments that can be found in soil, water or air samples. The concept of eDNA is based on the fact that organisms constantly leave organic traces behind them. By analysing the eDNA, researchers can detect the presence of any species in that area. This method is a valuable technique for biodiversity monitoring and ecological research.    Unlike traditional methods that involve direct observation or capturing of organisms, eDNA analysis allows researchers to study biodiversity without directly interacting with the species. This non-invasive approach is particularly useful for studying elusive or endangered species. eDNA analysis has been applied in various ecosystems, including freshwater, marine, and terrestrial environments. It is used to study aquatic organisms, t
Read more

Deep Neural Networks for ADMET properties' prediction

By -
Image
Introduction The absorption, distribution, metabolism, excretion and toxicity properties of any compound contributes to its potential as an orally administered drug. A set of criteria defined by Rule of Five or the Pfizer's Rule of Five stats that any compound meeting these criteria are more likely to have ADMET properties. The importance of these properties has increased in modern medicinal chemistry and new machine learning techniques are employed to predict these properties. The predictive ADMET models have helped in discovery of small molecules with improved safety and dose. Deep Neural Networks are showing usefulness in such predictions due to improved computational efficiency, larger datasets and adaption of image processing in the chemistry. Early Models The early DNNs employed showed improved prediction performance. Many researchers raised concerned that this improvement is in fact has no relation with better computational capacity, rather it is result of mere memorizing of
Read more

How Visual Cortex inspired the Convolutional Neural Networks

By -
Image
Introduction Since the 1960s, after the successful development (at least with respect to the existing technologies of that time) of the first perceptron by Frank Rosenblatt, researchers around the world have been trying to make computers more brain-like. In other words, they are constantly trying to develop programs and hardware that work much like our brains. However, creating a machine that thinks and programs like a human brain is still impossible, given the limitations of present technologies. But, we are successful in this direction up to an agreeable extent. The perceptron developed by Rosenblatt can be considered one of the first implementations of neural networks. It was a huge hardware model, rather than a Python program like today's perceptrons.  Rosenblatt's perceptron was a simple binary input-output system, with various limitations. It was not trainable for different patterns, rather but for simple image recognization. The major drawback was its inability to perfor
Read more