Automated detection and sorting of microencapsulation <i>via</i> machine learning

Phase change materials (PCMs) store latent heat energy as they melt and release it upon freezing. However, they suffer from chemical instability and poor thermal conductivity, which can be improved by encapsulation. Here, we encapsulated a salt hydrate PCM (Mg(NO 3 ) 2 ·6H 2 O) within all-silica nanocapsules using a Pickering emulsion template. Electron microscopy analysis demonstrated robust silica–silica (RSS) shell formed inner silica layer of approximately 45 nm thickness, with silica Pickering emulsifiers anchored to the surface. The RSS nanostructured capsules are 300–1000 nm in size and have far superior thermal and chemical stability compared with that of the bulk salt hydrate. Differential scanning calorimetry showed encapsulated PCMs were stable over 500+ melt/freeze cycles (equivalent to 500+ day/night temperature difference) with a latent heat of 112.8 J·g –1 . Thermogravimetric analysis displayed their impressive thermal stability, with as little as 37.2% mass loss at 800 °C. Raman spectroscopy proved the presence of salt hydrate within RSS capsules and illustrated the improved chemical stability compared to non-encapsulated Mg(NO 3 ) 2 ·6H 2 O. Energy capsule behavior compared with the bulk material was also observed at the macroscale with thermal imaging, showing that the melting/freezing behavior of the PCM is confined to the nanocapsule core. The thermal conductivity of the silica shell measured by laser flash thermal conductivity method is 1.4 ± 0.2 W·(m·K) −1 , which is around 7 times more than the thermal conductivity of the polymer shell (0.2 W·(m·K) −1 ). RSS capsules containing PCMs have improved thermal stability and conductivity compared to polymer-based capsules and have good potential for thermoregulation or energy storage applications.

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“…These free particles comprise silica and do not absorb latent heat in the studied temperature range. 56 …”

Section: Resultsmentioning

confidence: 99%

Highly Stable Energy Capsules with Nano-SiO₂ Pickering Shell for Thermal Energy Storage and Release

et al. 2020

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“…Machine learning offers a route to enable automated monitoring of microfluidic systems by converting routinely collected sensor and image data into actionable information in real time [99]. It has been demonstrated very recently how machine learning-assisted image analysis can facilitate quality control over droplet generation [100] and efficiently code droplet populations [101]. With machine learning-supported image analysis, experimental conditions in microfluidic droplet assays can be encoded and decoded by colored beads [101].…”

Section: Challenges and Opportunities Facing Droplet Microfluidicsmentioning

confidence: 99%

Droplet Microfluidics-Enabled High-Throughput Screening for Protein Engineering

Weng

Spoonamore

2019

Micromachines

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Protein engineering—the process of developing useful or valuable proteins—has successfully created a wide range of proteins tailored to specific agricultural, industrial, and biomedical applications. Protein engineering may rely on rational techniques informed by structural models, phylogenic information, or computational methods or it may rely upon random techniques such as chemical mutation, DNA shuffling, error prone polymerase chain reaction (PCR), etc. The increasing capabilities of rational protein design coupled to the rapid production of large variant libraries have seriously challenged the capacity of traditional screening and selection techniques. Similarly, random approaches based on directed evolution, which relies on the Darwinian principles of mutation and selection to steer proteins toward desired traits, also requires the screening of very large libraries of mutants to be truly effective. For either rational or random approaches, the highest possible screening throughput facilitates efficient protein engineering strategies. In the last decade, high-throughput screening (HTS) for protein engineering has been leveraging the emerging technologies of droplet microfluidics. Droplet microfluidics, featuring controlled formation and manipulation of nano- to femtoliter droplets of one fluid phase in another, has presented a new paradigm for screening, providing increased throughput, reduced reagent volume, and scalability. We review here the recent droplet microfluidics-based HTS systems developed for protein engineering, particularly directed evolution. The current review can also serve as a tutorial guide for protein engineers and molecular biologists who need a droplet microfluidics-based HTS system for their specific applications but may not have prior knowledge about microfluidics. In the end, several challenges and opportunities are identified to motivate the continued innovation of microfluidics with implications for protein engineering.

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“…Similarly, for distinction of particular cell phenotypes from complex samples with similar-sized cells [21], there is a need for routines that can be trained using model cell types to enable impedance-based signal recognition. Machine learning offers a route to enable automated monitoring of microfluidic systems by converting routinely collected sensor and image data into actionable information in real time [22,23]. Machine learning has been extensively used for fast image processing in microscopy and flow cytometry applications (see [24][25][26][27][28]).…”

Section: Introductionmentioning

confidence: 99%

A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry

et al. 2020

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Microfluidic applications such as active particle sorting or selective enrichment require particle classification techniques that are capable of working in real time. In this paper, we explore the use of neural networks for fast label-free particle characterization during microfluidic impedance cytometry. A recurrent neural network is designed to process data from a novel impedance chip layout for enabling real-time multiparametric analysis of the measured impedance data streams. As demonstrated with both synthetic and experimental datasets, the trained network is able to characterize with good accuracy size, velocity, and crosssectional position of beads, red blood cells, and yeasts, with a unitary prediction time of 0.4 ms. The proposed approach can be extended to other device designs and cell types for electrical parameter extraction. This combination of microfluidic impedance cytometry and machine learning can serve as a stepping stone to real-time single-cell analysis and sorting.

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Automated detection and sorting of microencapsulation via machine learning

Abstract: We automated a traditionally labor-intensive, yet widely-used capsule production system.

Cited by 36 publications

References 44 publications

Highly Stable Energy Capsules with Nano-SiO₂ Pickering Shell for Thermal Energy Storage and Release

Highly Stable Energy Capsules with Nano-SiO₂ Pickering Shell for Thermal Energy Storage and Release

Droplet Microfluidics-Enabled High-Throughput Screening for Protein Engineering

A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry

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Automated detection and sorting of microencapsulation via machine learning

Abstract: We automated a traditionally labor-intensive, yet widely-used capsule production system.

Cited by 36 publications

References 44 publications

Highly Stable Energy Capsules with Nano-SiO2 Pickering Shell for Thermal Energy Storage and Release

Highly Stable Energy Capsules with Nano-SiO2 Pickering Shell for Thermal Energy Storage and Release

Droplet Microfluidics-Enabled High-Throughput Screening for Protein Engineering

A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry

Contact Info

Product

Resources

About

Highly Stable Energy Capsules with Nano-SiO₂ Pickering Shell for Thermal Energy Storage and Release

Highly Stable Energy Capsules with Nano-SiO₂ Pickering Shell for Thermal Energy Storage and Release