convergent flow microfluidic chip  (Microfluidic ChipShop)

Journal: ACS Omega

Article Title: 3D-Printed Microfiltration Membranes via Dual-Wavelength Microstereolithography

doi: 10.1021/acsomega.5c05746

Figure Lengend Snippet: (a) Schematic illustration of the microfluidic emulsion fabrication setup to prepare monodisperse emulsions using a commercial microfluidic chip (focused-flow droplet generator), along with the microscopy image of the fabricated emulsion (scale bar is 50 μm)). (b) Optical microscopy image of the chip used for emulsion fabrication.

Article Snippet: Monodisperse O/W emulsion is fabricated using a focused-flow droplet generator microfluidic chip (Micronit Microfluidics B.V., The Netherlands).

Techniques: Emulsion, Microscopy

Journal: Advanced Science

Article Title: A Machine Vision Perspective on Droplet‐Based Microfluidics

doi: 10.1002/advs.202413146

Figure Lengend Snippet: Conception of the machine vision on Microfluidic droplets. a) An overview of this study. The trained machine vision can identify both single and DE droplets, as well as other core–shell structures. The original images here for identification demonstration come from Liang's, [ ⁵⁴ ] Zhang's, [ ⁶⁰ ] Mettler's, [ ⁵⁶ ] and our experimental works. Reproduced with permission. [ ⁵⁴ ] Copyright 2024, Wiley. Reproduced with permission. [ ⁶⁰ ] Copyright 2018, American Institute of Physics. b) Machine vision framework consisting of training, object detection, and data processing modules. High‐resolution images are acquired using high‐speed optical imaging to generate a large input image dataset for training the Deformable DETR. After training, raw images are delivered into the Deformable DETR module where the microfluidic droplets in the raw images can be identified. The detected data then, undergoes data processing module where useful parameters of the detected droplets can be obtained.

Article Snippet: Note (Supporting Information) details our five established microfluidic systems for generating multiple microfluidic droplets including normal oil–water–oil (o–w–o) DEs, nanoberries [ ] encapsulated liposomal DEs, SDs, phase change material (PCM) encapsulated DEs, cell encapsulated droplets.

Techniques: Optical Imaging

Journal: Advanced Science

Article Title: A Machine Vision Perspective on Droplet‐Based Microfluidics

doi: 10.1002/advs.202413146

Figure Lengend Snippet: Schematics of manually labeled and machine identified microfluidic droplets and evaluation system. a) Typical manually labeled microfluidic droplets for training and evaluation. Microfluidic droplet images for various scenarios such as SDs and DE droplets (single inner droplet and multiple inner droplets) were adopted for manual annotation with rectangular manually labeled boxes for wide applications of microfluidic droplet research and industry. b) Rectangular schematic manually labeled, and machine identified boxes with coordinate information. c) Intersection area (filled by orange) and d) Union area (filled by yellow) between the manually labeled and machine identified boxes. e) examples of poor, good, and excellent identification with IoU values.

Article Snippet: Note (Supporting Information) details our five established microfluidic systems for generating multiple microfluidic droplets including normal oil–water–oil (o–w–o) DEs, nanoberries [ ] encapsulated liposomal DEs, SDs, phase change material (PCM) encapsulated DEs, cell encapsulated droplets.

Techniques: Labeling

Journal: Advanced Science

Article Title: A Machine Vision Perspective on Droplet‐Based Microfluidics

doi: 10.1002/advs.202413146

Figure Lengend Snippet: Single‐image‐based Identification of microfluidic droplets and analysis from multiple scenarios. a–i) Machine identification of the DE droplets with inner cores and statistical analyses based on the identification results. The DE droplet images in (a, b, c, d, e, f, g, h, and i) are originally from Metter's, [ ⁵⁶ ] Lashkaripour's, [ ⁴¹ ] Kim's, [ ⁵⁷ ] Fu's, [ ⁵⁸ ] Hughes's, [ ⁵⁹ ] Zhang's, [ ⁶⁰ ] Foster's, [ ⁶¹ ] Zarzar's, [ ⁶² ] and Zhang's [ ⁶³ ] works, respectively. Reproduced with permission. [ ⁴¹ ] Copyright 2024, Springer Nature. Reproduced with permission. [ ⁵⁸ ] Copyright 2014, Elsevier. Reproduced with permission. [ ⁵⁹ ] Copyright 2013, Elsevier. Reproduced with permission. [ ⁶⁰ ] Copyright 2018, American Institute of Physics. Reproduced with permission. [ ⁶¹ ] Copyright 2010, Elsevier. Reproduced with permission. [ ⁶² ] Copyright 2017, The National Academy of Sciences of The United States of America. Reproduced with permission. [ ⁶³ ] Copyright 2019, The National Academy of Sciences USA. The scale bars in (a, b, c, d, and e) represent 100, 50, 100, 200, and 100 µm, respectively. The white and black scale bars in (i) represent 100 µm. The deep blue MIBs denote the outer droplets of DEs, and the black ones are inner droplets of DEs. The red ones represent the SDs.

Article Snippet: Note (Supporting Information) details our five established microfluidic systems for generating multiple microfluidic droplets including normal oil–water–oil (o–w–o) DEs, nanoberries [ ] encapsulated liposomal DEs, SDs, phase change material (PCM) encapsulated DEs, cell encapsulated droplets.

Techniques:

Journal: Advanced Science

Article Title: A Machine Vision Perspective on Droplet‐Based Microfluidics

doi: 10.1002/advs.202413146

Figure Lengend Snippet: Detailed data information for microfluidic DEs in Figure . Data distribution and box plots for microfluidic droplets in a) Figure , [ ⁶⁰ ] b) Figure , [ ⁶¹ ] c) Figure h, [ ⁶² ] and d) Figure . [ ⁶³ ]

Article Snippet: Note (Supporting Information) details our five established microfluidic systems for generating multiple microfluidic droplets including normal oil–water–oil (o–w–o) DEs, nanoberries [ ] encapsulated liposomal DEs, SDs, phase change material (PCM) encapsulated DEs, cell encapsulated droplets.

Techniques:

Journal: Advanced Science

Article Title: A Machine Vision Perspective on Droplet‐Based Microfluidics

doi: 10.1002/advs.202413146

Figure Lengend Snippet: Movie‐based machine Identification of microfluidic droplets and analysis from multiple scenarios. a–d) Identification of DE and SD droplets and statistical analysis based on our own experiments. In (a), Q o , Q m , and Q i = 166.6, 50.00, and 33.33 µL min −1 . In (b), Q o , Q m , and Q i = 100.0, 50.00, and 50.00 µL min −1 . In (c), Q o , Q m , and Q i = 100.0, 300.0, and 300.0 µL min −1 . In (d), Q o , Q m , and Q i = 100.0, 400.0, and 700.0 µL min −1 . e) Identification of DE droplets and statistical analysis based on Utada et al.’s work. [ ⁶⁵ ] Reproduced with permission. [ ⁶⁵ ] Copyright 2005, American Association for the Advancement of Science. f) Identification of single cell encapsulation using our own experiments. g) Identification of SD droplets based on Kim et al.’s work. [ ⁵⁷ ] Reproduced with permission. [ ⁵⁷ ] Copyright 2014, Elsevier. h) Identification of DE droplets before and after light‐triggered reactions based on Chen et al.’s work. [ ⁶⁶ ] Reproduced with permission. [ ⁶⁶ ] Copyright 2023, Elsevier. The identification images in (a–h) are representative still images for different scenarios in Movie (Supporting Information). The green line in each image functions as a reference line, which helps to calculate the generation frequency of the microfluidic droplets. The green arrow represents the flow direction of the microfluidic droplets.

Article Snippet: Note (Supporting Information) details our five established microfluidic systems for generating multiple microfluidic droplets including normal oil–water–oil (o–w–o) DEs, nanoberries [ ] encapsulated liposomal DEs, SDs, phase change material (PCM) encapsulated DEs, cell encapsulated droplets.

Techniques: Encapsulation