Automated image segmentation-assisted flattening of atomic force microscopy images

Laser Controlled Manipulation of Microbubbles on a Surface with Silica-Coated Gold Nanoparticle Array

Microbubble generation and manipulation play critical roles in diverse applications such as microfluidic mixing, pumping, and microrobot propulsion. However, existing methods are typically limited to lateral movements on customized substrates or rely on specific liquids with particular properties or designed concentration gradients, thereby hindering their practical applications. To address this challenge, this paper presents a method that enables robust vertical manipulation of microbubbles. By focusing a resonant laser on hydrophilic silica-coated gold nanoparticle arrays immersed in water, plasmonic microbubbles are generated and detach from the substrates immediately upon cessation of laser irradiation. Using simple laser pulse control, it can achieve an adjustable size and frequency of bubble bouncing, which is governed by the movement of the three-phase contact line during surface wetting. Furthermore, it demonstrates that rising bubbles can be pulled back by laser irradiation induced thermal Marangoni flow, which is verified by particle image velocimetry measurements and numerical simulations. This study provides novel insights into flexible bubble manipulation and integration in microfluidics, with significant implications for various applications including mixing, drug delivery, and the development of soft actuators.

dStripe: Slice artefact correction in diffusion MRI via constrained neural network

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dStripe allows removing inter-slice intensity artefacts in the presence of motion.

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It is not tied to a particular q-space sampling scheme or motion correction method.

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Can be trained in the absence of ground truth data.

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Uses explicit constraints that locally preserve in-plane image contrast.

MRI scanner and sequence imperfections and advances in reconstruction and imaging techniques to increase motion robustness can lead to inter-slice intensity variations in Echo Planar Imaging. Leveraging deep convolutional neural networks as universal image filters, we present a data-driven method for the correction of acquisition artefacts that manifest as inter-slice inconsistencies, regardless of their origin. This technique can be applied to motion- and dropout-artefacted data by embedding it in a reconstruction pipeline. The network is trained in the absence of ground-truth data on, and finally applied to, the reconstructed multi-shell high angular resolution diffusion imaging signal to produce a corrective slice intensity modulation field. This correction can be performed in either motion-corrected or scattered source-space. We focus on gaining control over the learned filter and the image data consistency via built-in spatial frequency and intensity constraints. The end product is a corrected image reconstructed from the original raw data, modulated by a multiplicative field that can be inspected and verified to match the expected features of the artefact. In-plane, the correction approximately preserves the contrast of the diffusion signal and throughout the image series, it reduces inter-slice inconsistencies within and across subjects without biasing the data. We apply our pipeline to enhance the super-resolution reconstruction of neonatal multi-shell high angular resolution data as acquired in the developing Human Connectome Project.

The role of convolutional neural networks in scanning probe microscopy: a review

Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy measures multimodal surface properties, combining morphology with electronic, mechanical, and other characteristics. In this review, we focus on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.

Acquisition and processing of high-speed atomic force microscopy videos for single amyloid aggregate observation

The structural dynamics of the amyloid protein aggregation process are associated with neurodegenerative diseases, including Alzheimer's disease and Parkinson's disease. High-speed atomic force microscopy (HS-AFM) is able to visualize the structural dynamics of individual aggregate species that otherwise cannot be distinguished. HS-AFM observations also detect impurities in the sample, and thus, experiments require relatively high sample purity. To derive valid information regarding the structural dynamics of the sample from the high-speed AFM images, a correction of the influence caused by the drift of the stage (scanner) from all frames is required. However, correcting the HS-AFM videos that consist of a large number of images requires significant effort. Here, using HS-AFM observation of α-synuclein fibril elongation as an example, we propose an HS-AFM image processing procedure to correct stage drift in the x-, y-, and z-directions with the free software ImageJ. ImageJ with default settings and our plugins attached to this article can process and analyze image stacks, which allow users to easily detect and show the temporal change in sample structures. This processing method can be automatically applied to numerous HS-AFM videos by batch processing with a series of ImageJ macrofunctions.

Laser photonic nanojets triggered thermoplasmonic micro/nanofabrication of polymer materials for enhanced resolution

Micro/nanofabrication of polymer materials is of interest for micro/nanofluidic systems. Due to the optical diffraction limit, it remains a challenge to achieve nanoscale resolution fabrication using an ordinary continuous-wave laser system. In this study, we therefore propose a laser photonic nanojet-based micro/nanofabrication method for polymer materials using a low-power and low-cost continuous-wave laser. The photonic nanojets were produced using glass microspheres. Moreover, a thermoplasmonic effect was employed by depositing a gold layer beneath the polymer films. By applying the photonic nanojet triggered thermoplasmonics, sub-micrometer surface structures, as well as their arrays, were fabricated with a laser power threshold value down to 10 mW. The influences of the microsphere diameters, and thicknesses of gold layers and polymer films on the fabricated microstructures were systematically investigated, which aligns well with the finite-difference time-domain simulation results.

Giant plasmonic bubbles nucleation under different ambient pressures

Water-immersed gold nanoparticles irradiated by a laser can trigger the nucleation of plasmonic bubbles after a delay time of a few microseconds [Wang et al., Proc. Natl. Acad. Sci. USA 122, 9253 (2018)]. Here we systematically investigated the light-vapor conversion efficiency, η, of these plasmonic bubbles as a function of the ambient pressure. The efficiency of the formation of these initial-phase and mainly water-vapor containing bubbles, which is defined as the ratio of the energy that is required to form the vapor bubbles and the total energy dumped in the gold nanoparticles before nucleation of the bubble by the laser, can be as high as 25%. The amount of vaporized water first scales linearly with the total laser energy dumped in the gold nanoparticles before nucleation, but for larger energies the amount of vaporized water levels off. The efficiency η decreases with increasing ambient pressure. The experimental observations can be quantitatively understood within a theoretical framework based on the thermal diffusion equation and the thermal dynamics of the phase transition.

Plasmonic Bubble Nucleation and Growth in Water: Effect of Dissolved Air

Under continuous laser irradiation, noble metal nanoparticles immersed in water can quickly heat up, leading to the nucleation of so-called plasmonic bubbles. In this work, we want to further understand the bubble nucleation and growth mechanism. In particular, we quantitatively study the effect of the amount of dissolved air on the bubble nucleation and growth dynamics, both for the initial giant bubble, which forms shortly after switching on the laser and is mainly composed of vapor, and for the final life phase of the bubble, during which it mainly contains air expelled from water. We found that the bubble nucleation temperature depends on the gas concentration: the higher the gas concentration, the lower the bubble nucleation temperature. Also, the long-term diffusion-dominated bubble growth is governed by the gas concentration. The radius of the bubbles grows as R(t) ∝ t ^1/3 for air-equilibrated and air-oversaturated water. In contrast, in partially degassed water, the growth is much slower since, even for the highest temperature we achieve, the water remains undersaturated.