Design for Low-Temperature Microwave-Assisted Crystallization of Ceramic Thin Films


  • Author: Nathan Nakamura, Jason Seepaul, Joseph B. Kadane, B. Reeja‐Jayan
  • Date: 21 June 2018
  • Copyright: image appears courtesy of Getty Images

Authors of a paper recently published in Applied Stochastic Models in Business and Industry have designed experiments to determine optimized values for input parameters such as temperature, solution concentration, and power input for synthesizing ceramic materials, specifically titanium dioxide (TiO2) thin films using microwave radiation. These permit crystallization of these films at significantly lower temperatures (150‐160 °C) compared to conventional techniques (>450 °C).

The authors explain their findings in further detail below. 

thumbnail image: Design for Low-Temperature Microwave-Assisted Crystallization of Ceramic Thin Films

The properties and functionality of materials are dependent on both the composition and processing parameters utilized during material growth. The ability to predict experimental conditions to obtain desirable material properties is of the utmost importance, as it enables efficient design of materials and devices by saving both experimental time and resources. This is especially critical in materials synthesis processes, where a myriad of input conditions can have significant impact on the resultant material composition and performance, and accurate modeling of these processes significantly reduces the amount of trial-and-error necessary to obtain the desired material. In this paper, the authors apply regression modeling to study the effects of different reaction inputs on ceramic thin film growth during microwave radiation (MWR)-assisted synthesis.

MWR-assisted synthesis is a rapid, low-temperature synthesis technique that enables ceramic growth with reduced energy input requirements relative to conventional high-temperature ceramic growth methods. Understanding how reaction conditions affect materials grown using MWR-assisted synthesis is particularly relevant, as the fundamental mechanisms underlying synthesis of ceramic films in the presence of MWR are not well understood. Regression analysis is applied using inputs such as reaction temperature, applied MWR power, and reaction time to predict conditions that will maximize the amount of surface coverage of the thin film material. The analysis yielded a set of optimal conditions that increased surface coverage relative to the parameters attempted previously. In the future, similar types of regression models can be applied to a wider range of materials and processes, and used to predict conditions that maximize a greater variety of relevant material properties. These models can help accelerate the design of materials by allowing future researchers to predict input parameters that tune properties and functionality to fit their application.

The full article can be found here: 

Design for low‐temperature microwave‐assisted crystallization of ceramic thin films

Applied Stochastic Models in Business and Industry, Volume 33, Issue 3

Special Issue: In honor of Kathryn Chaloner

May/June 2017

Pages 314-321

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