Chirality Analysis of Complex Microparticles using Deep Learning on Realistic Sets of Microscopy Images

Size-Resolved Shape Evolution in Inorganic Nanocrystals Captured via High-Throughput Deep Learning-Driven Statistical Characterization

Precise size and shape control in nanocrystal synthesis is essential for utilizing nanocrystals in various industrial applications, such as catalysis, sensing, and energy conversion. However, traditional ensemble measurements often overlook the subtle size and shape distributions of individual nanocrystals, hindering the establishment of robust structure-property relationships. In this study, we uncover intricate shape evolutions and growth mechanisms in Co3O4 nanocrystal synthesis at a subnanometer scale, enabled by deep-learning-assisted statistical characterization. By first controlling synthetic parameters such as cobalt precursor concentration and water amount then using high resolution electron microscopy imaging to identify the geometric features of individual nanocrystals, this study provides insights into the interplay between synthesis conditions and the size-dependent shape evolution in colloidal nanocrystals. Utilizing population-wide imaging data encompassing over 441,067 nanocrystals, we analyze their characteristics and elucidate previously unobserved size-resolved shape evolution. This high-throughput statistical analysis is essential for representing the entire population accurately and enables the study of the size dependency of growth regimes in shaping nanocrystals. Our findings provide experimental quantification of the growth regime transition based on the size of the crystals, specifically (i) for faceting and (ii) from thermodynamic to kinetic, as evidenced by transitions from convex to concave polyhedral crystals. Additionally, we introduce the concept of an "onset radius," which describes the critical size thresholds at which these transitions occur. This discovery has implications beyond achieving nanocrystals with desired morphology; it enables finely tuned correlation between geometry and material properties, advancing the field of colloidal nanocrystal synthesis and its applications.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

Chiral nanomaterials in tissue engineering

Addressing significant medical challenges arising from tissue damage and organ failure, the field of tissue engineering has evolved to provide revolutionary approaches for regenerating functional tissues and organs. This involves employing various techniques, including the development and application of novel nanomaterials. Among them, chiral nanomaterials comprising non-superimposable nanostructures with their mirror images have recently emerged as innovative biomaterial candidates to guide tissue regeneration due to their unique characteristics. Chiral nanomaterials including chiral fibre supramolecular hydrogels, polymer-based chiral materials, self-assembling peptides, chiral-patterned surfaces, and the recently developed intrinsically chiroptical nanoparticles have demonstrated remarkable ability to regulate biological processes through routes such as enantioselective catalysis and enhanced antibacterial activity. Despite several recent reviews on chiral nanomaterials, limited attention has been given to the specific potential of these materials in facilitating tissue regeneration processes. Thus, this timely review aims to fill this gap by exploring the fundamental characteristics of chiral nanomaterials, including their chiroptical activities and analytical techniques. Also, the recent advancements in incorporating these materials in tissue engineering applications are highlighted. The review concludes by critically discussing the outlook of utilizing chiral nanomaterials in guiding future strategies for tissue engineering design.

Geometric Control and Optical Properties of Intrinsically Chiral Plasmonic Nanomaterials

Intrinsically chiral plasmonic nanomaterials exhibit intriguing geometry-dependent chiroptical properties, which is due to the combination of plasmonic features with geometric chirality. Thus, chiral plasmonic nanomaterials have become promising candidates for applications in biosensing, asymmetric catalysis, biomedicine, photonics, etc. Recent advances in geometric control and optical tuning of intrinsically chiral plasmonic nanomaterials have further opened up a unique opportunity for their widespread applications in many emerging technological areas. Here, the recent developments in the geometric control of chiral plasmonic nanomaterials are reviewed with special attention given to the quantitative understanding of the chiroptical structure-property relationship. Several important optical spectroscopic tools for characterizing the optical chirality of plasmonic nanomaterials at both ensemble and single-particle levels are also discussed. Three emerging applications of chiral plasmonic nanomaterials, including enantioselective sensing, enantioselective catalysis, and biomedicine, are further highlighted. It is envisioned that these advanced studies in chiral plasmonic nanomaterials will pave the way toward the rational design of chiral nanomaterials with desired optical properties for diverse emerging technological applications.