Large-scale and controllable fabrication is an indispensable step for the industrialization and commercialization of halide perovskite nanocrystals, which are new-generation semiconductor materials for optoelectronic applications. Microfluidics, which provides continuous and precise synthesis, has been considered as a promising technique to fulfill this aspect. The research studies over the past decades have witnessed the advancement of microfluidics as a powerful tool in the fabrication of halide perovskite nanocrystals. In this Perspective, the state-of-the-art research based on microfluidics is introduced initially, including the synthesis of functional structures and materials, devices, as well as the interdisciplinary interactions between microfluidics and artificial intelligence and machine learning, etc. We then detail the issues and challenges in hindering progress in the above areas. Finally, we provide future directions and trends for the technology to achieve its full potential. This Perspective is expected to benefit the collective efforts between the field of nanomaterials and microfluidics in advanced manufacturing.
This content will become publicly available on October 31, 2025
Designing and generating new data under targeted properties has been attracting various critical applications such as molecule design, image editing and speech synthesis. Traditional hand-crafted approaches heavily rely on expertise experience and intensive human efforts, yet still suffer from the insufficiency of scientific knowledge and low throughput to support effective and efficient data generation. Recently, the advancement of deep learning has created the opportunity for expressive methods to learn the underlying representation and properties of data. Such capability provides new ways of determining the mutual relationship between the structural patterns and functional properties of the data and leveraging such relationships to generate structural data, given the desired properties. This article is a systematic review that explains this promising research area, commonly known as controllable deep data generation. First, the article raises the potential challenges and provides preliminaries. Then the article formally defines controllable deep data generation, proposes a taxonomy on various techniques and summarizes the evaluation metrics in this specific domain. After that, the article introduces exciting applications of controllable deep data generation, experimentally analyzes and compares existing works. Finally, this article highlights the promising future directions of controllable deep data generation and identifies five potential challenges.
more » « less- NSF-PAR ID:
- 10520955
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Computing Surveys
- Volume:
- 56
- Issue:
- 9
- ISSN:
- 0360-0300
- Page Range / eLocation ID:
- 1 to 38
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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