Enhanced light collection in fluorescence microscopy using self-assembled micro-reflectors

Assembly of multicomponent structures from hundreds of micron-scale building blocks using optical tweezers

The fabrication of three-dimensional (3D) microscale structures is critical for many applications, including strong and lightweight material development, medical device fabrication, microrobotics, and photonic applications. While 3D microfabrication has seen progress over the past decades, complex multicomponent integration with small or hierarchical feature sizes is still a challenge. In this study, an optical positioning and linking (OPAL) platform based on optical tweezers is used to precisely fabricate 3D microstructures from two types of micron-scale building blocks linked by biochemical interactions. A computer-controlled interface with rapid on-the-fly automated recalibration routines maintains accuracy even after placing many building blocks. OPAL achieves a 60-nm positional accuracy by optimizing the molecular functionalization and laser power. A two-component structure consisting of 448 1-µm building blocks is assembled, representing the largest number of building blocks used to date in 3D optical tweezer microassembly. Although optical tweezers have previously been used for microfabrication, those results were generally restricted to single-material structures composed of a relatively small number of larger-sized building blocks, with little discussion of critical process parameters. It is anticipated that OPAL will enable the assembly, augmentation, and repair of microstructures composed of specialty micro/nanomaterial building blocks to be used in new photonic, microfluidic, and biomedical devices.

Metal-enhanced bright-field microscopy

Transparent samples pose serious challenges in bright-field microscopic imaging due to their low optical contrast, which also affects the imaging speed. While the role of the substrate in enhancing optical contrast has been investigated for imaging modalities such as fluorescence, a similar analysis for bright-field imaging has not been conducted. Here, we explore the effect of the substrate on the optical contrast and imaging speed in bright-field microscopic imaging. We develop a simulation model to explain the significant improvement in these imaging parameters by the introduction of reflective back substrates. We explain the effects using energy density distribution plots, power coupling, and scattering. Further, we explore the notion of optical contrast in the case of microfeatures and a procedure for selecting an appropriate metric to quantify the same. This work concludes with experimental results showing a twofold improvement in contrast and a fivefold improvement in imaging rate by an appropriate choice of back substrate.

Encapsulated Nanodroplets for Enhanced Fluorescence Detection by Nano-Extraction

Enhancement of the detection signal of fluorescence microscopy in highly diluted solutions is of great importance in chemical analysis, sensing, and bioassay applications. Surface nanodroplets with atto- to femto-liter volumes are promising tools for sensitive online detection by integrating their extremely efficient nano-extraction and optical advantages. In this paper, the development of novel basic units of nanodroplets-in-a-microdroplet by simple solvent exchange is reported. The encapsulated nanodroplets are applied for ultrasensitive and online detection in fluorescence imaging. The biphasic nature of the droplet composite enables simultaneous extraction and enrichment of both hydrophobic and hydrophilic compounds. Furthermore, the desirable lensing effect of the curved surface of the nanodroplets enhances the collection of light emitted from the fluorophore extracted in the droplets by ≈60-fold, allowing sensitive and quantitative analysis of the fluorophore using fluorescence microscopy. The results highlight the potential of encapsulated nanodroplets as a simple and innovative method of signal enhancement in chemical analysis. By integrating selective concentration, extraction, and sensitive detection, the encapsulated nanodroplets reported here may have broad applications in many chemical and biological matrices.

Optimized sensing of sparse and small targets using lens-free holographic microscopy

Lens-free holographic microscopy offers sub-micron resolution over an ultra-large field-of-view >20 mm², making it suitable for bio-sensing applications that require the detection of small targets at low concentrations. Various pixel super-resolution techniques have been shown to enhance resolution and boost signal-to-noise ratio (SNR) by combining multiple partially-redundant low-resolution frames. However, it has been unclear which technique performs best for small-target sensing. Here, we quantitatively compare SNR and resolution in experiments using no regularization, cardinal-neighbor regularization, and a novel implementation of sparsity-promoting regularization that uses analytically-calculated gradients from Bayer-pattern image sensors. We find that sparsity-promoting regularization enhances the SNR by ~8 dB compared to the other methods when imaging micron-scale beads with surface coverages up to ~4%.

Lensless digital holographic microscopy and its applications in biomedicine and environmental monitoring

Optical compound microscope has been a major tool in biomedical imaging for centuries. Its performance relies on relatively complicated, bulky and expensive lenses and alignment mechanics. In contrast, the lensless microscope digitally reconstructs microscopic images of specimens without using any lenses, as a result of which it can be made much smaller, lighter and lower-cost. Furthermore, the limited space-bandwidth product of objective lenses in a conventional microscope can be significantly surpassed by a lensless microscope. Such lensless imaging designs have enabled high-resolution and high-throughput imaging of specimens using compact, portable and cost-effective devices to potentially address various point-of-care, global-health and telemedicine related challenges. In this review, we discuss the operation principles and the methods behind lensless digital holographic on-chip microscopy. We also go over various applications that are enabled by cost-effective and compact implementations of lensless microscopy, including some recent work on air quality monitoring, which utilized machine learning for high-throughput and accurate quantification of particulate matter in air. Finally, we conclude with a brief future outlook of this computational imaging technology.

Extended Solution Gate OFET-based Biosensor for Label-free Glial Fibrillary Acidic Protein Detection with Polyethylene Glycol-Containing Bioreceptor Layer

A novel organic field effect transistor (OFET) -based biosensor is described for label-free glial fibrillary acidic protein (GFAP) detection. We report the first use of an extended solution gate structure where the sensing area and the organic semiconductor are separated, and a reference electrode is not needed. Different molecular weight polyethylene glycols (PEGs) are mixed into the bio-receptor layer to help extend the Debye screening length. The drain current change was significantly increased with the help of higher molecular weight PEGs, as they are known to reduce the dielectric constant. We also investigated the sensing performance under different gate voltage (V_g). The sensitivity increased after we decreased V_g from -5 V to -2 V, because the lower V_g is much closer to the OFET threshold voltage and the influence of attached negatively charged proteins become more apparent. Finally, the selectivity experiments toward different interferents were performed. The stability and selectivity are promising for clinical applications.

Unconventional methods of imaging: computational microscopy and compact implementations

In the past two decades or so, there has been a renaissance of optical microscopy research and development. Much work has been done in an effort to improve the resolution and sensitivity of microscopes, while at the same time to introduce new imaging modalities, and make existing imaging systems more efficient and more accessible. In this review, we look at two particular aspects of this renaissance: computational imaging techniques and compact imaging platforms. In many cases, these aspects go hand-in-hand because the use of computational techniques can simplify the demands placed on optical hardware in obtaining a desired imaging performance. In the first main section, we cover lens-based computational imaging, in particular, light-field microscopy, structured illumination, synthetic aperture, Fourier ptychography, and compressive imaging. In the second main section, we review lensfree holographic on-chip imaging, including how images are reconstructed, phase recovery techniques, and integration with smart substrates for more advanced imaging tasks. In the third main section we describe how these and other microscopy modalities have been implemented in compact and field-portable devices, often based around smartphones. Finally, we conclude with some comments about opportunities and demand for better results, and where we believe the field is heading.

Improving the Sensitivity and Functionality of Mobile Webcam-Based Fluorescence Detectors for Point-of-Care Diagnostics in Global Health

Resource-poor countries and regions require effective, low-cost diagnostic devices for accurate identification and diagnosis of health conditions. Optical detection technologies used for many types of biological and clinical analysis can play a significant role in addressing this need, but must be sufficiently affordable and portable for use in global health settings. Most current clinical optical imaging technologies are accurate and sensitive, but also expensive and difficult to adapt for use in these settings. These challenges can be mitigated by taking advantage of affordable consumer electronics mobile devices such as webcams, mobile phones, charge-coupled device (CCD) cameras, lasers, and LEDs. Low-cost, portable multi-wavelength fluorescence plate readers have been developed for many applications including detection of microbial toxins such as C. Botulinum A neurotoxin, Shiga toxin, and S. aureus enterotoxin B (SEB), and flow cytometry has been used to detect very low cell concentrations. However, the relatively low sensitivities of these devices limit their clinical utility. We have developed several approaches to improve their sensitivity presented here for webcam based fluorescence detectors, including (1) image stacking to improve signal-to-noise ratios; (2) lasers to enable fluorescence excitation for flow cytometry; and (3) streak imaging to capture the trajectory of a single cell, enabling imaging sensors with high noise levels to detect rare cell events. These approaches can also help to overcome some of the limitations of other low-cost optical detection technologies such as CCD or phone-based detectors (like high noise levels or low sensitivities), and provide for their use in low-cost medical diagnostics in resource-poor settings.

Signal enhanced holographic fluorescence microscopy with guide-star reconstruction

We propose a signal enhanced guide-star reconstruction method for holographic fluorescence microscopy. In the late 00's, incoherent digital holography started to be vigorously studied by several groups to overcome the limitations of conventional digital holography. The basic concept of incoherent digital holography is to acquire the complex hologram from incoherent light by utilizing temporal coherency of a spatially incoherent light source. The advent of incoherent digital holography opened new possibility of holographic fluorescence microscopy (HFM), which was difficult to achieve with conventional digital holography. However there has been an important issue of low and noisy signal in HFM which slows down the system speed and degrades the imaging quality. When guide-star reconstruction is adopted, the image reconstruction gives an improved result compared to the conventional propagation reconstruction method. The guide-star reconstruction method gives higher imaging signal-to-noise ratio since the acquired complex point spread function provides optimal system-adaptive information and can restore the signal buried in the noise more efficiently. We present theoretical explanation and simulation as well as experimental results.