Description:[0001] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0002] Machine learning is a branch of artificial intelligence that allows computers to learn from data and improve over time. Machine learning algorithms can detect patterns in data and use them to make predictions. Machine learning algorithms can be trained on data sets to create models that allow machines to perform tasks that would otherwise only be possible for humans.
[0003] A number of different types of nanoparticle utilization and its catalyst efficacy systems that are known in the prior art. For example, the following patents are provided for their supportive teachings and are all incorporated by reference.
[0004] Fe3O4@SiO2-PMA-Cu magnetic nanoparticles as a novel catalyst for green synthesis of β-thiol-1,4-disubstituted-1,2,3-triazoles: - The magnetic nanoparticles of Fe3O4 were synthesized through a solid-state reaction of hydrated iron (III) chloride, hydrated iron (II) chloride and NaOH, and then purified by calcination at high temperature. In order to protect ferrite nanoparticles from oxidation and agglomeration, and to manufacture a novel catalytic system of anchored copper on the magnetic substrate, the Fe3O4 was core-shelled by adding tetraethyl orthosilicate. Next, the prepared Fe3O4@SiO2 was supported by phosphomolybdic acid (PMA) as the second layer of nanocomposite at 80 °C in 30 h. Eventually, the new nanocomposite of Fe3O4@SiO2-PMA-Cu was successfully synthesized by adding copper (II) chloride solution and solid potassium borohydride. The structure of magnetic Nano catalyst was acknowledged through different techniques such as EDS, VSM, XRD, TEM, FT-IR, XPS, TGA, BET and FESEM. The synthesis of β-thiolo/benzyl-1,2,3-triazoles from various thiiranes, terminal alkynes and sodium azide was catalyzed by Fe3O4@SiO2-PMA-Cu nanocomposite in aqueous medium. In order to obtain the optimum condition, the effects of reaction time, temperature, catalyst amount and solvent were gauged. The recycled catalyst was used for several consecutive runs without any loss of activity.
[0005] Fe3O4@SiO2-PMA-Cu magnetic nanoparticles are a novel catalyst for the green synthesis of β-thiol-1,4-disubstituted-1,2,3-triazoles. Fe3O4@SiO2-PMA-Cu nanoparticles can also catalyze the synthesis of β-thiolo/benzyl-1,2,3-triazoles from various thiiranes, terminal alkynes, and sodium azide. Magnetite (Fe3O4) nanoparticles are attractive nanomaterials in the fields of material science, chemistry, and physics. The proposed invention focuses on analyzing the utilisation of Fe3O4@SiO2-PMA-Cu nanoparticles as an eco-friendly catalyst for β-Thiol-1,4-Disubstituted-1,2,3-Triazole Green Synthesis through algorithms of Machine Learning.
[0006] Above information is presented as background information only to assist with an understanding of the present disclosure. No determination has been made, no assertion is made, and as to whether any of the above might be applicable as prior art with regard to the present invention.
[0007] In the view of the foregoing disadvantages inherent in the known types of nanoparticle utilization and its catalyst efficacy systems now present in the prior art, the present invention provides an improved system. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved machine learning approach to predict the utilisation of Fe3O4@SiO2-PMA-Cu nanoparticles as an eco-friendly catalyst for β-Thiol-1,4-Disubstituted-1,2,3-Triazole Green Synthesis that has all the advantages of the prior art and none of the disadvantages.
SUMMARY OF INVENTION
[0008] In the view of the foregoing disadvantages inherent in the known types of nanoparticle utilization and its catalyst efficacy analysis systems now present in the prior art, the present invention provides an improved one. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved Machine learning approach to predict the utilisation of Fe3O4@SiO2-PMA-Cu nanoparticles as an eco-friendly catalyst for β-Thiol-1,4-Disubstituted-1,2,3-Triazole Green Synthesis which has all the advantages of the prior art and none of the disadvantages.
[0009] The main objective of the proposed invention is to design & implement a framework of machine learning techniques for analysing the utilisation of Fe3O4@SiO2-PMA-Cu nanoparticles as an eco-friendly catalyst for β-Thiol-1,4-Disubstituted-1,2,3-Triazole Green Synthesis. The utilisation of Fe3O4@SiO2-PMA-Cu nanoparticles as an eco-friendly catalyst is analyzed.
[0010] Yet another important aspect of the proposed invention is to design & implement a framework of Machine Learning techniques that will consider on understanding the functions of Green Synthesis from Green Chemistry Triumph. The utilisation of Fe3O4@SiO2-PMA-Cu nanoparticles as an eco-friendly catalyst is analyzed by predictive unit. The results of prediction are displayed on the display unit.
[0011] In this respect, before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the various ways. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[0012] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be had to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF DRAWINGS
[0013] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
Figure 1 illustrates the schematic view of Machine learning based technique to predict the utilisation of Fe3O4@SiO2-PMA-Cu nanoparticles as an eco-friendly catalyst for β-Thiol-1,4-Disubstituted-1,2,3-Triazole Green Synthesis from Green Chemistry Triumph, according to the embodiment herein.
DETAILED DESCRIPTION OF INVENTION
[0014] In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that the embodiments may be combined, or that other embodiments may be utilized and that structural and logical changes may be made without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims and their equivalents.
[0015] While the present invention is described herein by way of example using several embodiments and illustrative drawings, those skilled in the art will recognize that the invention is neither intended to be limited to the embodiments of drawing or drawings described, nor intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention covers all modification/s, equivalents and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. The headings are used for organizational purposes only and are not meant to limit the scope of the description or the claims. As used throughout this description, the word "may" be used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Further, the words "a" or "a" mean "at least one” and the word “plurality” means one or more, unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and any additional subject matter not recited, and is not intended to exclude any other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like are included in the specification solely for the purpose of providing a context for the present invention.
[0016] In this disclosure, whenever an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same element or group of elements with transitional phrases "consisting essentially of, "consisting", "selected from the group consisting of”, "including", or "is" preceding the recitation of the element or group of elements and vice versa.
[0017] Green synthesis is a chemical engineering and chemistry field that aims to create products and processes that reduce the use and production of hazardous substances. It is also an environmentally friendly method that aims to reduce energy consumption, eliminate toxic waste, and use ecological solvents. Green synthesis uses a clean, safe, and cost-effective process to construct nanomaterials.
[0018] Green chemistry is a branch of chemistry that focuses on designing products and processes to reduce or eliminate the use of hazardous substances. It's also known as sustainable chemistry or circular chemistry. Green chemistry applies to the entire life cycle of a chemical product, including its design, manufacture, use, and disposal. It aims to make working with chemicals less dangerous for people and the environment. The proposed invention focuses on implementing the algorithms of Machine learning for studying the functions of Green Synthesis from Green Chemistry Triumph.
[0019] Reference will now be made in detail to the exemplary embodiment of the present disclosure. Before describing the detailed embodiments that are in accordance with the present disclosure, it should be observed that the embodiment resides primarily in combinations arrangement of the system according to an embodiment herein and as exemplified in FIG. 1
[0020] Figure 1 illustrates the schematic view of Machine learning based technique to predict the utilisation of Fe3O4@SiO2-PMA-Cu nanoparticles as an eco-friendly catalyst for β-Thiol-1,4-Disubstituted-1,2,3-Triazole Green Synthesis from Green Chemistry Triumph 100. The proposed invention 100 includes system of green chemistry 101 and analysed for proper utilisation of Fe3O4@SiO2-PMA-Cu nanoparticles 102 as an eco-friendly catalyst for β-Thiol-1,4-Disubstituted-1,2,3-Triazole Green Synthesis 103. The machine learning unit 104 will make its prediction regarding utilisation using the predictive algorithm 105. The results of predictive unit 105 is displayed on the display unit 106.
[0021] In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide a thorough understanding of the arrangement of the system according to an embodiment herein. It will be apparent, however, to one skilled in the art that the present embodiment can be practiced without these specific details. In other instances, structures are shown in block diagram form only in order to avoid obscuring the present invention.
, Claims:1. Machine learning based technique to predict the utilisation of Fe3O4@SiO2-PMA-Cu nanoparticles as an eco-friendly catalyst for β-Thiol-1,4-Disubstituted-1,2,3-Triazole Green Synthesis from Green Chemistry Triumph, comprises of:
Machine learning unit;
Predictive unit and
Display unit.
2. Machine learning based technique to predict the utilisation of Fe3O4@SiO2-PMA-Cu nanoparticles as an eco-friendly catalyst for β-Thiol-1,4-Disubstituted-1,2,3-Triazole Green Synthesis from Green Chemistry Triumph, according to claim 1, includes a machine learning unit, wherein the machine learning unit will make its prediction regarding utilisation using the predictive algorithm.
3. Machine learning based technique to predict the utilisation of Fe3O4@SiO2-PMA-Cu nanoparticles as an eco-friendly catalyst for β-Thiol-1,4-Disubstituted-1,2,3-Triazole Green Synthesis from Green Chemistry Triumph, according to claim 1, includes a predictive unit, wherein the predictive unit will predict the utilisation of Fe3O4@SiO2-PMA-Cu nanoparticles as an eco-friendly catalyst for β-Thiol-1,4-Disubstituted-1,2,3-Triazole Green Synthesis from Green Chemistry Triumph.
4. Machine learning based technique to predict the utilisation of Fe3O4@SiO2-PMA-Cu nanoparticles as an eco-friendly catalyst for β-Thiol-1,4-Disubstituted-1,2,3-Triazole Green Synthesis from Green Chemistry Triumph, according to claim 1, includes a display unit, wherein the display unit will display the results of predictive unit.