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Lens-free holographic microscopy (LFHM) is cost-effective and field-portable, making it a promising diagnostic approach for point-of-care applications. However, LFHM has not yet been applied to protein molecule sensing in solution . Here we develop a quantitative large-area binding sensor by combining a high-speed LFHM with a one-step bead-based agglutination assay, where agglutination of >10^4 2-μm beads in solution undergoing Brownian motion are imaged and quantified. We sense NeutrAvidin molecules and interferon-gamma (an immune system biomarker) in solution, achieving a limit of detection of

Accurate, cost-effective, easy-to-use, and point-of-care sensors for protein biomarker levels are important for disease diagnostics. A cost-effective and compact readout approach that has been used for several diagnostic applications is lens-free holographic microscopy, which provides an ultralarge field of view and submicron resolution when it is coupled with pixel super-resolution techniques. Despite its potential as a diagnostic technique, lens-free microscopy has not previously been applied to quantitative protein molecule sensing in solution, which can simplify sensing protocols and ultimately enable measurements of binding kinetics in physiological conditions. Here, we sense interferon-γ (an immune system biomarker) and NeutrAvidin molecules in solution by combining lens-free microscopy with a one-step bead-based agglutination assay, enabled by a custom high-speed light-emitting diode (LED) array and automated image processing routines. We call this a quantitative large-area binding (QLAB) sensor. The high-speed light source provides, for the first time, pixel super-resolved imaging of >104 2 μm beads in solution undergoing Brownian motion, without significant motion blur. The automated image processing routines enable the counting of individual beads and clusters, providing a quantitative sensor readout that depends on both bead and analyte concentrations. Fits to the chemical binding theory are provided. For NeutrAvidin, we find a limit of detection (LOD) of

Lens-free on-chip imaging devices provide cost-effective, compact, and wide-field microscopy solutions for fieldwork and global health applications.

Aggregation of > 104 beads due to specific protein molecules is quantified using microfluidic chips, fast lens-free microscopes, and image processing algorithms. The limit of detection, cost, and size are appropriate for COVID-19 point-of-care testing.

The ability to sense, count, and size microscopic and nanoscopic particles is important in air quality monitoring, biomedical diagnostics, and nanomaterials synthesis. Lensfree holographic microscopy is an attractive sensing platform due to its ultra-large field of view, compact form factor, and cost-effective components. Although submicron resolution has been previously demonstrated using lensfree holographic microscopy, the ability to detect individual microscale and nanoscale objects can pose a challenge due to limited signal to noise ratio (SNR). Previously, we have used vapor-deposited nanoscale polymer lenses to boost the SNR in sensing experiments, however this adds experimental complexity and is not compatible with all types of samples. Here we present a computational approach for boosting SNR in lensfree holographic microscopy. This approach optimizes a sparsity-promoting cost function in conjunction with a pixel superresolution method for synthesizing a high resolution hologram out of multiple low-resolution holograms captured at slightly different angles. The resulting high-resolution hologram can be computationally reconstructed to provide an in-focus image of the sample. We find that a sparsity-promoting cost function yields ~8 dB of improvement over conventional pixel superresolution approaches that involve cardinal neighbor regularization, provided that the surface coverage is below ~4%. The impacts of the sparsity-promoting cost function on image resolution and computational time will be presented, as well as a guide to which regularization parameters work best for given target sizes and coverage densities. These computational approaches can be used to extend the limit of detection of lensfree holographic microscopes in sensing applications.

Three major challenges in biological sensing and imaging systems are to improve sensitivity, resolution, and throughput. In this talk, I present results on using structures seeded or constructed by nanoparticles to provide enhancements in these three areas. We present our work in two parts. First, we have used vapor-deposited liquid nanoscale polyethylene glycol lenses to aid in sensing nanoscale particles that serve as seeds for these nanolenses. This approach is combined with lensfree holographic microscopy to image a transparent sample slide with adsorbed nanoparticles. Lensfree holographic microscopy provides an ultra-large field of view >20 mm^2 together with submicron resolution, however its sensitivity to nanoparticles is limited and particles smaller than ~300 nm cannot be inherently distinguished from background noise. The liquid nanolenses we deposit significantly enhance the sensitivity of this system such that >10^5 particles as small as 40 nm can be individually detected, localized, and sized. The second part of this talk focuses on using optical tweezers to assemble nanophotonic structures out of heterogeneous colloidal nanoparticles. The resulting structures can provide superresolution based on transmission line and metamaterial physical principles. Optically-positioned nanoparticles can also be attached to whispering gallery mode microresonators to enhance their sensitivity and facilitate free-space coupling. Finally, we have investigated the speed with which we can position nanoparticles using optical tweezers, and have measured maximum manipulation rates in excess of 150 um/s, which makes optical tweezers a viable approach for rapid prototyping additive manufacturing of nanophotonic structures.

High-resolution optical microscopy has traditionally relied on high-magnification and high–numerical aperture objective lenses. In contrast, lensless microscopy can provide high-resolution images without the use of any focusing lenses, offering the advantages of a large field of view, high resolution, cost-effectiveness, portability, and depth-resolved three-dimensional (3D) imaging. Here we review various approaches to lensless imaging, as well as its applications in biosensing, diagnostics, and cytometry. These approaches include shadow imaging, fluorescence, holography, superresolution 3D imaging, iterative phase recovery, and color imaging. These approaches share a reliance on computational techniques, which are typically necessary to reconstruct meaningful images from the raw data captured by digital image sensors. When these approaches are combined with physical innovations in sample preparation and fabrication, lensless imaging can be used to image and sense cells, viruses, nanoparticles, and biomolecules. We conclude by discussing several ways in which lensless imaging and sensing might develop in the near future.

Lens-free holographic microscopy offers sub-micron resolution over a field-of-view >20 mm2, making it a suitable biomedical imaging and sensing platform. We devised a sparsity-promoting method, which enhances SNR by ∼ 8 dB compared to traditional methods.

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%.

Lens-free holographic microscopy is a promising diagnostic approach because it is cost-effective, compact, and suitable for point-of-care applications, while providing high resolution together with an ultra-large field-of-view. It has been applied to biomedical sensing, where larger targets like eukaryotic cells, bacteria, or viruses can be directly imaged without labels, and smaller targets like proteins or DNA strands can be detected via scattering labels like micro- or nano-spheres. Automated image processing routines can count objects and infer target concentrations. In these sensing applications, sensitivity and specificity are critically affected by image resolution and signal-to-noise ratio (SNR). Pixel super-resolution approaches have been shown to boost resolution and SNR by synthesizing a high-resolution image from multiple, partially redundant, low-resolution images. However, there are several computational methods that can be used to synthesize the high-resolution image, and previously, it has been unclear which methods work best for the particular case of small-particle sensing. Here, we quantify the SNR achieved in small-particle sensing using regularized gradient-descent optimization method, where the regularization is based on cardinal-neighbor differences, Bayer-pattern noise reduction, or sparsity in the image. In particular, we find that gradient-descent with sparsity-based regularization works best for small-particle sensing. These computational approaches were evaluated on images acquired using a lens-free microscope that we assembled from an off-the-shelf LED array and color image sensor. Compared to other lens-free imaging systems, our hardware integration, calibration, and sample preparation are particularly simple. We believe our results will help to enable the best performance in lens-free holographic sensing.

Caused by the herpes simplex virus (HSV), herpes is a viral infection that is one of the most widespread diseases worldwide. Here we present a computational sensing technique for specific detection of HSV using both viral immuno-specificity and the physical size range of the viruses. This label-free approach involves a compact and cost-effective holographic on-chip microscope and a surface-functionalized glass substrate prepared to specifically capture the target viruses. To enhance the optical signatures of individual viruses and increase their signal-to-noise ratio, self-assembled polyethylene glycol based nanolenses are rapidly formed around each virus particle captured on the substrate using a portable interface. Holographic shadows of specifically captured viruses that are surrounded by these self-assembled nanolenses are then reconstructed, and the phase image is used for automated quantification of the size of each particle within our large field-of-view, ~30 mm2. The combination of viral immuno-specificity due to surface functionalization and the physical size measurements enabled by holographic imaging is used to sensitively detect and enumerate HSV particles using our compact and cost-effective platform. This computational sensing technique can find numerous uses in global health related applications in resource-limited environments.

Herpes is one of the most widespread sexually transmitted viral diseases. Timely detection of Herpes Simplex Virus (HSV) can help prevent the rampant spreading of the virus. Current detection techniques such as viral culture, immuno-assays or Polymerase-Chain-Reaction, are time extensive and require expert handling. Here we present a field-portable, easy-to-use, and cost-effective biosensor for the detection of HSV based on holographic imaging. The virus is first captured from a target solution onto specifically developed substrates, prepared by coating glass coverslips with HSV-specific antibodies, and imaged using a lensfree holographic microscope. Several light-emitting-diodes (LEDs), coupled to multi-mode optical-fibers, are used to illuminate the sample containing the viruses. A micro-controller is used to activate the LEDs one at a time and in-line holograms are recorded using a CMOS imager placed immediately above the substrate. These sub-pixel shifted holograms are used to generate a super-resolved hologram, which is reconstructed to obtain the phase and amplitude images of the viruses. The signal of the viruses is enhanced using self-assembled PEG-based nanolenses, formed around the viral particles. Based on the phase information of the reconstructed images we can estimate the size of the viral particles, with an accuracy of ± 11 nm, as well as quantify the viral load. The limit-of-detection of this system is estimated to be

Significant progress in characterization of nanoparticles and biomolecules was enabled by the development of advanced imaging equipment with extreme spatial-resolution and sensitivity. To perform some of these analyses outside of well-resourced laboratories, it is necessary to create robust and cost-effective alternatives to existing high-end laboratory-bound imaging and sensing equipment. Towards this aim, we have designed a holographic on-chip microscope operating at an ultraviolet illumination wavelength (UV) of 266 nm. The increased forward scattering from nanoscale objects at this short wavelength has enabled us to detect individual sub-30 nm nanoparticles over a large field-of-view of >16 mm2 using an on-chip imaging platform, where the sample is placed at ≤0.5 mm away from the active area of an opto-electronic sensor-array, without any lenses in between. The strong absorption of this UV wavelength by biomolecules including nucleic acids and proteins has further enabled high-contrast imaging of nanoscopic aggregates of biomolecules, e.g., of enzyme Cu/Zn-superoxide dismutase, abnormal aggregation of which is linked to amyotrophic lateral sclerosis (ALS) - a fatal neurodegenerative disease. This UV-based wide-field computational imaging platform could be valuable for numerous applications in biomedical sciences and environmental monitoring, including disease diagnostics, viral load measurements as well as air- and water-quality assessment.

Ultraviolet light enhances particle imaging capabilities of on-chip microscopy, increasing the detection sensitivity to sub-30 nm particles. Ultraviolet illumination also enables high contrast imaging of biomolecules, e.g., protein aggregates, over a large field-of-view.

The direct observation of nanoscale objects is a challenging task for optical microscopy because the scattering from an individual nanoparticle is typically weak at optical wavelengths. Electron microscopy therefore remains one of the gold standard visualization methods for nanoparticles, despite its high cost, limited throughput and restricted field-of-view. Here, we describe a high-throughput, on-chip detection scheme that uses biocompatible wetting films to self-assemble aspheric liquid nanolenses around individual nanoparticles to enhance the contrast between the scattered and background light. We model the effect of the nanolens as a spatial phase mask centred on the particle and show that the holographic diffraction pattern of this effective phase mask allows detection of sub-100 nm particles across a large field-of-view of >20 mm2. As a proof-of-concept demonstration, we report on-chip detection of individual polystyrene nanoparticles, adenoviruses and influenza A (H1N1) viral particles.

The sizing of individual nanoparticles and the recovery of the distributions of sizes from populations of nanoparticles provide valuable information in virology, exosome analysis, air and water quality monitoring, and nanomaterials synthesis. Conventional approaches for nanoparticle sizing include those based on costly or low-throughput laboratory-scale equipment such as transmission electron microscopy or nanoparticle tracking analysis, as well as those approaches that only provide population-averaged quantities, such as dynamic light scattering. Some of these limitations can be overcome using a new family of alternative approaches based on quantitative phase imaging that combines lensfree holographic on-chip microscopy with self-assembled liquid nanolenses. In these approaches, the particles of interest are deposited onto a glass coverslip and the sample is coated with either pure liquid polyethylene glycol (PEG) or aqueous solutions of PEG. Due to surface tension, the PEG self-assembles into nano-scale lenses around the particles of interest. These nanolenses enhance the scattering signatures of the embedded particles such that individual nanoparticles as small as 40 nm are clearly visible in phase images reconstructed from captured holograms. The magnitude of the phase quantitatively corresponds to particle size with an accuracy of +/-11 nm. This family of approaches can individually size more than 10^5 particles in parallel, can handle a large dynamic range of particle sizes (40 nm – 100s of microns), and can accurately size multi-modal distributions of particles. Furthermore, the entire approach has been implemented in a compact and cost-effective device suitable for use in the field or in low-resource settings.

Giardia lamblia is a waterborne parasite that causes an intestinal infection, known as giardiasis, and it is found not only in countries with inadequate sanitation and unsafe water but also streams and lakes of developed countries. Simple, sensitive, and rapid detection of this pathogen is important for monitoring of drinking water. Here we present a cost-effective and field portable mobile-phone based fluorescence microscopy platform designed for automated detection of Giardia lamblia cysts in large volume water samples (i.e., 10 ml) to be used in low-resource field settings. This fluorescence microscope is integrated with a disposable water-sampling cassette, which is based on a flow-through porous polycarbonate membrane and provides a wide surface area for fluorescence imaging and enumeration of the captured Giardia cysts on the membrane. Water sample of interest, containing fluorescently labeled Giardia cysts, is introduced into the absorbent pads that are in contact with the membrane in the cassette by capillary action, which eliminates the need for electrically driven flow for sample processing. Our fluorescence microscope weighs ~170 grams in total and has all the components of a regular microscope, capable of detecting individual fluorescently labeled cysts under light-emitting-diode (LED) based excitation. Including all the sample preparation, labeling and imaging steps, the entire measurement takes less than one hour for a sample volume of 10 ml. This mobile phone based compact and cost-effective fluorescent imaging platform together with its machine learning based cyst counting interface is easy to use and can even work in resource limited and field settings for spatio-temporal monitoring of water quality.

In fluorescence microscopy, the signal-to-noise ratio (SNR) of the optical system is directly linked to the numerical aperture (NA) of the microscope objective, which creates detection challenges for low-NA, wide-field and high-throughput imaging systems. Here we demonstrate a method to increase the light collection efficiency from micron-scale fluorescent objects using self-assembled vapor-condensed polyethylene glycol droplets, which act as micro-reflectors for fluorescent light. Around each fluorescent particle, a liquid meniscus is formed that increases the excitation efficiency and redirects part of the laterally-emitted fluorescent light towards the detector due to internal reflections at the liquid-air interface of the meniscus. The three-dimensional shape of this micro-reflector can be tuned as a function of time, vapor temperature and substrate contact angle, providing us optimized SNR performance for fluorescent detection. Based on these self-assembled micro-reflectors, we experimentally demonstrate ~2.5-3 fold enhancement of the fluorescent signal from 2-10 μm sized particles. A theoretical explanation of the formation rate and shapes of these micro-reflectors is presented, along with a ray tracing model of their optical performance. This method can be used as a sample preparation technique for consumer electronics-based microscopy and sensing tools, thus increasing the sensitivity of low-NA systems that image fluorescent micro-objects.

Sizing individual nanoparticles and dispersions of nanoparticles provides invaluable information in applications such as nanomaterial synthesis, air and water quality monitoring, virology, and medical diagnostics. Several conventional nanoparticle sizing approaches exist; however, there remains a lack of high-throughput approaches that are suitable for low-resource and field settings, i.e., methods that are cost-effective, portable, and can measure widely varying particle sizes and concentrations. Here we fill this gap using an unconventional approach that combines holographic on-chip microscopy with vapor-condensed nanolens self-assembly inside a cost-effective hand-held device. By using this approach and capturing time-resolved in situ images of the particles, we optimize the nanolens formation process, resulting in significant signal enhancement for the label-free detection and sizing of individual deeply subwavelength particles (smaller than lambda/10) over a 30 mm(2) sample field-of-view, with an accuracy of +/- 11 nm. These time-resolved measurements are significantly more reliable than a single measurement at a given time, which was previously used only for nanoparticle detection without sizing. We experimentally demonstrate the sizing of individual nanoparticles as well as viruses, monodisperse samples, and complex polydisperse mixtures, where the sample concentrations can span similar to 5 orders-of-magnitude and particle sizes can range from 40 nm to millimeter-scale. We believe that this high-throughput and label-free nanoparticle sizing platform, together with its cost-effective and hand-held interface, will make highly advanced nanoscopic measurements readily accessible to researchers in developing countries and even to citizen-scientists, and might especially be valuable for environmental and biomedical applications as well as for higher education and training programs.

Wide-field imaging facilitates the detection of rare events and reduces imaging time for large-area samples. Nano-scale imaging enables the detection and enumeration of individual nanoparticles and sub-cellular bioparticles. Uniting these two capabilities in a compact and field-portable on-chip imaging platform through lensfree holographic microscopy provides deep submicron resolution with high sensitivity over fields of view in the range 10–1800 mm2 – approximately 150–2700 times larger than a typical 40 × objective field of view. High resolution is achieved via a pixel super-resolution approach, while high sensitivity is obtained via a sample preparation procedure that generates self-assembled nanolenses around individual nanoparticles and viruses. The foundations of these computational imaging approaches, along with their methodology and results, are discussed.

Vapor-condensed nanolenses make label-free lensfree holographic microscopy sensitive to individual particles 40 nm and larger. Detected signals correlate with particle size with accuracy +/- 11 nm. Automated image processing measures >10(5) particles within a single field-of-view.

Rapid and sensitive detection of waterborne pathogens in drinkable and recreational water sources is crucial for treating and preventing the spread of water related diseases, especially in resource-limited settings. Here we present a field-portable and cost-effective platform for detection and quantification of Giardia lamblia cysts, one of the most common waterborne parasites, which has a thick cell wall that makes it resistant to most water disinfection techniques including chlorination. The platform consists of a smartphone coupled with an opto-mechanical attachment weighing ~205 g, which utilizes a hand-held fluorescence microscope design aligned with the camera unit of the smartphone to image custom-designed disposable water sample cassettes. Each sample cassette is composed of absorbent pads and mechanical filter membranes; a membrane with 8 μm pore size is used as a porous spacing layer to prevent the backflow of particles to the upper membrane, while the top membrane with 5 μm pore size is used to capture the individual Giardia cysts that are fluorescently labeled. A fluorescence image of the filter surface (field-of-view: ~0.8 cm(2)) is captured and wirelessly transmitted via the mobile-phone to our servers for rapid processing using a machine learning algorithm that is trained on statistical features of Giardia cysts to automatically detect and count the cysts captured on the membrane. The results are then transmitted back to the mobile-phone in less than 2 minutes and are displayed through a smart application running on the phone. This mobile platform, along with our custom-developed sample preparation protocol, enables analysis of large volumes of water (e.g., 10-20 mL) for automated detection and enumeration of Giardia cysts in ~1 hour, including all the steps of sample preparation and analysis. We evaluated the performance of this approach using flow-cytometer-enumerated Giardia-contaminated water samples, demonstrating an average cyst capture efficiency of ~79% on our filter membrane along with a machine learning based cyst counting sensitivity of ~84%, yielding a limit-of-detection of ~12 cysts per 10 mL. Providing rapid detection and quantification of microorganisms, this field-portable imaging and sensing platform running on a mobile-phone could be useful for water quality monitoring in field and resource-limited settings.

Optical imaging of sub-micron features requires high resolution and contrast. Using no imaging lenses, we achieve a numerical aperture of 0.92 over a large field-of-view (FOV) of e.g., >20 mm2. In parallel, we significantly enhance image contrast using self-assembled nanolenses, which form catenoids at the nanoscale, enabling sub-100-nm particle and virus detection over a wide FOV.

The physical interaction between nano-scale objects and liquid interfaces can create unique optical properties, enhancing the signatures of the objects with sub-wavelength features. Here we show that the evaporation on a wetting substrate of a polymer solution containing sub-micrometer or nano-scale particles creates liquid micro-lenses that arise from the local deformations of the continuous wetting film. These micro-lenses have properties similar to axicon lenses that are known to create beams with a long depth of focus. This enhanced depth of focus allows detection of single nanoparticles using a low magnification microscope objective lens, achieving a relatively wide field-of-view, while also lifting the constraints on precise focusing onto the object plane. Hence, by creating these liquid axicon lenses through spatial deformations of a continuous thin wetting film, we transfer the challenge of imaging individual nano-particles to detecting the light focused by these lenses. As a proof of concept, we demonstrate the detection and sizing of single nano-particles (100 and 200 nm), CpGV granuloviruses as well as Staphylococcus epidermidis bacteria over a wide field of view of e.g., 5.10×3.75 mm2 using a ×5 objective lens with a numerical aperture of 0.15. In addition to conventional lens-based microscopy, this continuous wetting film based approach is also applicable to lensfree computational on-chip imaging, which can be used to detect single nano-particles over a large field-of-view of e.g., >20-30 mm2. These results could be especially useful for high-throughput field-analysis of nano-scale objects using compact and cost-effective microscope designs.

The development of lensfree on-chip microscopy in the past decade has opened up various new possibilities for biomedical imaging across ultra-large fields of view using compact, portable, and cost-effective devices. However, until recently, its ability to resolve fine features and detect ultra-small particles has not rivalled the capabilities of the more expensive and bulky laboratory-grade optical microscopes. In this Frontier Review, we highlight the developments over the last two years that have enabled computational lensfree holographic on-chip microscopy to compete with and, in some cases, surpass conventional bright-field microscopy in its ability to image nano-scale objects across large fields of view, yielding giga-pixel phase and amplitude images. Lensfree microscopy has now achieved a numerical aperture as high as 0.92, with a spatial resolution as small as 225 nm across a large field of view e.g., >20 mm2. Furthermore, the combination of lensfree microscopy with self-assembled nanolenses, forming nano-catenoid minimal surfaces around individual nanoparticles has boosted the image contrast to levels high enough to permit bright-field imaging of individual particles smaller than 100 nm. These capabilities support a number of new applications, including, for example, the detection and sizing of individual virus particles using field-portable computational on-chip microscopes.

Computational microscopy tools, in particular lensfree on-chip imaging, provide a large field-of-view along with a long depth-of-field, which makes it feasible to rapidly analyze large volumes of specimen using a compact and light-weight on-chip imaging architecture. To bring molecular specificity to this high-throughput platform, here we demonstrate the use of plasmon-resonant metallic nanoparticles to automatically recognize different cell types based on their plasmon-enhanced lensfree holograms, detected and reconstructed over a large field-of-view of e.g., ~24 mm².

Individual unlabeled nanoparticles are observed within a >0.2–9 cm2 field-of-view by surrounding particles with self-assembled nanolenses and imaging them with super-resolved lensfree holographic computational microscopy. Nanolens formation, topology, and impact on contrast are discussed.

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.

Three-fold enhancement of fluorescent light collection in low numerical aperture optical systems is demonstrated using self-assembled micro-reflectors around individual micro-particles.