This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography.
more »
« less
A Review of Holography in the Aquatic Sciences: In situ Characterization of Particles, Plankton, and Small Scale Biophysical Interactions
The characterization of particle and plankton populations, as well as microscale biophysical interactions, is critical to several important research areas in oceanography and limnology. A growing number of aquatic researchers are turning to holography as a tool of choice to quantify particle fields in diverse environments, including but not limited to, studies on particle orientation, thin layers, phytoplankton blooms, and zooplankton distributions and behavior. Holography provides a non-intrusive, free-stream approach to imaging and characterizing aquatic particles, organisms, and behavior in situ at high resolution through a 3-D sampling volume. Compared to other imaging techniques, e.g., flow cytometry, much larger volumes of water can be processed over the same duration, resolving particle sizes ranging from a few microns to a few centimeters. Modern holographic imaging systems are compact enough to be deployed through various modes, including profiling/towed platforms, buoys, gliders, long-term observatories, or benthic landers. Limitations of the technique include the data-intensive hologram acquisition process, computationally expensive image reconstruction, and coherent noise associated with the holograms that can make post-processing challenging. However, continued processing refinements, rapid advancements in computing power, and development of powerful machine learning algorithms for particle/organism classification are paving the way for holography to be used ubiquitously across different disciplines in the aquatic sciences. This review aims to provide a comprehensive overview of holography in the context of aquatic studies, including historical developments, prior research applications, as well as advantages and limitations of the technique. Ongoing technological developments that can facilitate larger employment of this technique toward in situ measurements in the future, as well as potential applications in emerging research areas in the aquatic sciences are also discussed.
more »
« less
- PAR ID:
- 10281924
- Date Published:
- Journal Name:
- Frontiers in Marine Science
- Volume:
- 7
- ISSN:
- 2296-7745
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Imaging materials and biological structures in a liquid environment pose a significant challenge for conventional transmission electron microscopy (TEM) due to stringent requirement of ultrahigh vacuum design in the microscope column. The most recent liquid‐cell TEM technique, graphene liquid‐cell (GLC) microscopy, employs only layers of graphene to encapsulate liquid specimens. Recent efforts with GLC–TEM have demonstrated superior imaging resolution of beam‐sensitive specimens. Herein, the parameters that affect the quality of GLC analysis, including the graphene transfer onto TEM grids, are reviewed. Several important factors that affect the in situ TEM imaging of specimens, including the variations in GLC geometries and capillary pressure are discussed. The interaction between the electron beam and the liquid along with the possibility for artifacts or the formation of radical ions is also highlighted in this review. The scientific discoveries enabled by GLC–TEM in the areas of nucleation and growth of crystals, corrosion, battery science, as well as high‐resolution imaging of organelles and proteins are also briefly discussed. Finally, possible future research directions of GLC–TEM and the associated challenges are discussed. The synergistic effort to accomplish the proposed research directions has the potential to yield new discoveries in both materials and life sciences.more » « less
-
Understanding how particle size and morphology influence ion insertion dynamics is critical for a wide range of electrochemical applications including energy storage and electrochromic smart windows. One strategy to reveal such structure–property relationships is to perform ex situ transmission electron microscopy (TEM) of nanoparticles that have been cycled on TEM grid electrodes. One drawback of this approach is that images of some particles are correlated with the electrochemical response of the entire TEM grid electrode. The lack of one-to-one electrochemical-to-structural information complicates interpretation of genuine structure/property relationships. Developing high-throughput ex situ single particle-level analytical techniques that effectively link electrochemical behavior with structural properties could accelerate the discovery of critical structure-property relationships. Here, using Li-ion insertion in WO 3 nanorods as a model system, we demonstrate a correlated optically-detected electrochemistry and TEM technique that measures electrochemical behavior of via many particles simultaneously without having to make electrical contacts to single particles on the TEM grid. This correlated optical-TEM approach can link particle structure with electrochemical behavior at the single particle-level. Our measurements revealed significant electrochemical activity heterogeneity among particles. Single particle activity correlated with distinct local mechanical or electrical properties of the amorphous carbon film of the TEM grid, leading to active and inactive particles. The results are significant for correlated electrochemical/TEM imaging studies that aim to reveal structure-property relationships using single particle-level imaging and ensemble-level electrochemistry.more » « less
-
Ptychography is an enabling microscopy technique for both fundamental and applied sciences. In the past decade, it has become an indispensable imaging tool in most X-ray synchrotrons and national laboratories worldwide. However, ptychography’s limited resolution and throughput in the visible light regime have prevented its wide adoption in biomedical research. Recent developments in this technique have resolved these issues and offer turnkey solutions for high-throughput optical imaging with minimum hardware modifications. The demonstrated imaging throughput is now greater than that of a high-end whole slide scanner. In this review, we discuss the basic principle of ptychography and summarize the main milestones of its development. Different ptychographic implementations are categorized into four groups based on their lensless/lens-based configurations and coded-illumination/coded-detection operations. We also highlight the related biomedical applications, including digital pathology, drug screening, urinalysis, blood analysis, cytometric analysis, rare cell screening, cell culture monitoring, cell and tissue imaging in 2D and 3D, polarimetric analysis, among others. Ptychography for high-throughput optical imaging, currently in its early stages, will continue to improve in performance and expand in its applications. We conclude this review article by pointing out several directions for its future development.more » « less
-
Abstract Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.more » « less
An official website of the United States government

