Benjamin K. Wilson

Optical manipulation of micron/submicron sized particles and biomolecules through plasmonics.

Plasmonics, a rapidly emerging subdiscipline of nanophotonics, is aimed at exploiting surface plasmons for important applications, including sensing, waveguiding, and imaging. Parallel to these research efforts, technology yielding enhanced scattering and absorption of localized surface plasmons (LSPs) provides promising routes for trapping and manipulation of micro and nano scale particles, as well as biomolecules with low laser intensity due to high energy conversion efficiency under resonant excitation. In this paper, we show that the LSP-induced scattering field from a self-assembled gold nanoparticle array can be used to sustain trapping of single micron-sized particles with low laser intensity. Moreover, we demonstrate for the first time efficient localized concentration of sub-micron sized particles and DNAs of various sizes through photothermal effect of plasmonics.

Localized surface plasmon assisted microfluidic mixing

We present an optical microfluidic mixing approach via thermally induced convective flow sustained by localized surface plasmon (LSP) energy. The phonon energy associated with the nonradiative damping of LSP from a Au nanoparticle (NP) array under optical excitation creates a thermal gradient which initiates a convective fluidic flow. Experimental evidence and modeling results both show that LSP from the Au NPs is crucial in establishing a temperature gradient with sufficient magnitude to induce the convective flow with low input optical intensity.

Detection of malarial byproduct hemozoin utilizing its unique scattering properties

The scattering characteristics of the malaria byproduct hemozoin, including its scattering distribution and depolarization, are modeled using Discrete Dipole Approximation (DDA) and compared to those of healthy red blood cells. Scattering (or dark-field) spectroscopy and imaging are used to identify hemozoin in fresh rodent blood samples. A new detection method is proposed and demonstrated using dark-field in conjunction with cross-polarization imaging and spectroscopy. SNRs greater than 50:1 are achieved for hemozoin in fresh blood without the addition of stains or reagents. The potential of such a detection system is discussed.

Nanostructure-enhanced laser tweezers for efficient trapping and alignment of particles

We propose and demonstrate a purely optical approach to trap and align particles using the interaction of polarized light with periodic nanostructures to generate enhanced trapping force. With a weakly focused laser beam, we observed efficient trapping and transportation of polystyrene beads with sizes ranging from 10 μm down to 190 nm as well as cancer cell nuclei. In addition, alignment of non-spherical dielectric particles to a 1-D periodic nanostructure was achieved with low laser intensity without attachment to birefringent crystals. Bacterial cells were trapped and aligned with incident optical intensity as low as 17 μW/μm2.

Scalable nano-particle assembly by efficient light-induced concentration and fusion

Avalanche concentration, a rapid, long-range accumulation of particles around a laser spot in a liquid sample, is demonstrated and characterized for various nanoparticles (NPs). The effect is driven by a convective flow in the sample, caused by efficient heating of NPs with high absorption efficiencies. Several types of concentration behavior were observed and characterized. Control of optical power and initial particle density was found to be effective in determining the assembly process. VO(2) nanowires, carbon nanotube (CNT), and quantum dot (QD) electrode gap bridges were assembled with a variety of sizes and geometries to show the utility of the method for nano-assembly. Bridges were assembled from as many as thousands to as few as one NP and were found to form solid electrical contact between the electrodes, as verified by measuring the current--voltage (I-V) characteristic.

Automated microscopy and machine learning for expert-level malaria field diagnosis

The optical microscope is one of the most widely used tools for diagnosing infectious diseases in the developing world. Due to its reliance on trained microscopists, field microscopy often suffers from poor sensitivity, specificity, and reproducibility. The goal of this work, called the Autoscope, is a low-cost automated digital microscope coupled with a set of computer vision and classification algorithms, which can accurately diagnose of a variety of infectious diseases, targeting use-cases in the developing world. Our initial target is malaria, because of the high difficulty of the task and because manual microscopy is currently a central but highly imperfect tool for malaria work in the field. In addition to diagnosis, the algorithm performs species identification and quantitation of parasite load, parameters which are critical in many field applications but which are not effectively determined by rapid diagnostic tests (RDTs). We have built a hardware prototype which can scan approximately 0.1 μL of blood volume in a standard Giemsa-stained thick smear blood slide in approximately 20 minutes. We have also developed a comprehensive machine learning framework, leveraging computer vision and machine learning techniques including support vector machines (SVMs) and convolutional neural networks (CNNs). The Autoscope has undergone successful initial field testing for malaria diagnosis in Thailand.

Variable Wave Plate via Tunable Form-Birefringent Structures

We propose a compact and low-cost design for a variable wave plate using tunable form-birefringent grating structures in polydimethylsiloxane (PDMS). The described device operates through PDMS deformation via electrostatically actuated transparent electrodes. The optical and mechanical properties of tunable form-birefringent structures are modeled using birefringence, rigorous coupled-wave analysis, and simple structural mechanics theory. The device is fabricated by a form of soft nanoimprint lithography. The optical properties are characterized for different structure geometries. Operating principles were verified through testing of the completed device. The experimental results and theoretical study show that an irregular grating structure performs better than a periodic subwavelength structure. [2007-0303].

Trapping and Rotation of Nanowires Assisted by Surface Plasmons

We report long-range trapping of vanadium dioxide (VO2) and vanadium oxyhydroxide (H2V3O8) nanowires at a distance as large as 50 mum outside the laser spot using plasmonic tweezers and controlled rotation of the nanowires by combining trapping with microfluidic drag force. The plasmonic tweezers are built upon a self-assembled gold nanoparticle array platform. In addition to the long-range trapping and rotation capability, the required optical intensity for the plasmonic tweezers to initiate trapping is much lower (8 muW/mum2) than that required by conventional optical tweezers for similar nanowires. We also investigate possible mechanisms for the unique long-range trapping of nanowires through performing control experiments.

Characterization of cervigram image sharpness using multiple self-referenced measurements and random forest classifiers

Cervical cancer is the fourth most common cancer among women worldwide and is especially prevalent in low resource settings due to lack of screening and treatment options. Visual inspection with acetic acid (VIA) is a widespread and cost-effective screening method for cervical pre-cancer lesions, but accuracy depends on the experience level of the health worker. Digital cervicography, capturing images of the cervix, enables review by an off-site expert or potentially a machine learning algorithm. These reviews require images of sufficient quality. However, image quality varies greatly across users. A novel algorithm was developed to evaluate the sharpness of images captured with the MobileODT’s digital cervicography device (EVA System), in order to, eventually provide feedback to the health worker. The key challenges are that the algorithm evaluates only a single image of each cervix, it needs to be robust to the variability in cervix images and fast enough to run in real time on a mobile device, and the machine learning model needs to be small enough to fit on a mobile device’s memory, train on a small imbalanced dataset and run in real-time. In this paper, the focus scores of a preprocessed image and a Gaussian-blurred version of the image are calculated using established methods and used as features. A feature selection metric is proposed to select the top features which were then used in a random forest classifier to produce the final focus score. The resulting model, based on nine calculated focus scores, achieved significantly better accuracy than any single focus measure when tested on a holdout set of images. The area under the receiver operating characteristics curve was 0.9459.

Enhanced trapping and rotation of sub-micron particles and cells through nanostructures

In this work we propose and demonstrate the use of 1D photonic crystals to achieve both enhanced trapping forces and unique functionality, namely the ability to rotate and align particles using purely optical means. Particles as small as 190 nm can be trapped effectively, and bacteria cells can be rotated with an intensity as low as 17 µW/µm2.