Novel model for predicting the future volume of research articles on applications of Quantum Dots

The paper aims to briefly introduce to the topic of quantum heterostructures taking Quantum Dots (QD) as the topic of interest. It reviews different mathematical models explaining the behaviour of QD, and it proposes a novel method to predict the future article volumes on the topic of applications of QD. The paper also reviews the use of QD in quantum computing. The paper opted for a numerical approach to predict future article volumes. Firstly, bibliometric data for the past 40 years is collected and then by assuming a relationship between relevant variables, a governing equation is developed which is then solved using the Finite Difference Method (FDM). The paper provides insights into how a prediction model can be developed without using tons of metrics. It also suggests that a prediction model can be developed using only the past behaviour of the concerned dataset. Due to the chosen research approach, the effectiveness of the model may be less. Therefore, researchers are encouraged to test the proposed method further. The paper includes the practical implications of an easily analysed and executable model. The paper also shows that the model proposed can not only be used to predict future article volumes but also to predict datasets that exhibit a quasi-linear nature. This paper fulfils the need for a new, easier prediction method.