Existing methods for low cost lenses using parallel mold stamping and high temperature reflow requires complex engineering controls to produce high quality lenses. These manufacturing techniques rely on expensive equipment. In this paper, we propose a low cost (< $ 0.01 per pc) flexible moldless lens fabrication method based on curing a hanging transparent polydimethylsiloxane (PDMS) elastomer droplet on a curved substrate. Additional deposition of hanging droplets in the same manner led to a substantial increase in the lens curvature and concomitant decrease in the focal length of the PDMS lenses down to ~2 mm. The shortest focal length lenses were shown to collimate light from a bare light emitting diode (LED) and image microscopic structures down to around 4 µm with 160x magnification. Our hanging droplet lens fabrication technique heralds a new paradigm in the manufacture of low cost, high performance optical lenses for the masses. Using these lenses, we were able to transform an ordinary commercial smartphone camera into a low-cost digital dermascope (60x magnification) that can readily visualize microscopic structures on skin such as sweat pores.

The ability to label individual cells is useful for single-cell-level studies of complex cellular interactions and heterogeneity. Optically readable cell labeling is attractive as it can be interrogated noninvasively and repeatedly at high speeds. Here we demonstrate the feasibility of large-scale cell barcoding and identification using fluorescent polystyrene microbeads loaded into cells. Intracellular beads with different diameters in a range of 5 to 12 µm produce spectrally distinguished features or barcodes. A microfluidic chip was used to measure fluorescence resonance peaks emitted from individual cells. An algorithm comparing the peak wavelengths to a reference barcode library allowed barcode identification with high accuracy. This work provides guideline to increase the number of unique identifiers and reduce various false-positive, false-negative errors.

Intensity shot noise in digital holograms distorts the quality of the phase images after phase retrieval, limiting the usefulness of quantitative phase microscopy (QPM) systems in long term live cell imaging. In this paper, we devise a hologram-to-hologram neural network, Holo-UNet, that restores high quality digital holograms under high shot noise conditions (sub-mW/cm2 intensities) at high acquisition rates (sub-milliseconds). In comparison to current phase recovery methods, Holo-UNet denoises the recorded hologram, and so prevents shot noise from propagating through the phase retrieval step that in turn adversely affects phase and intensity images. Holo-UNet was tested on 2 independent QPM systems without any adjustment to the hardware setting. In both cases, Holo-UNet outperformed existing phase recovery and block-matching techniques by ∼ 1.8 folds in phase fidelity as measured by SSIM. Holo-UNet is immediately applicable to a wide range of other high-speed interferometric phase imaging techniques. The network paves the way towards the expansion of high-speed low light QPM biological imaging with minimal dependence on hardware constraints.