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      Real-time modulated nanoparticle separation with an ultra-large dynamic range

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          Abstract

          Real-time nanoparticle separation in deterministic lateral displacement can be achieved by manipulating solution iconicity.

          Abstract

          Nanoparticles exhibit size-dependent properties which make size-selective purification of proteins, DNA or synthetic nanoparticles essential for bio-analytics, clinical medicine, nano-plasmonics and nano-material sciences. Current purification methods of centrifugation, column chromatography and continuous-flow techniques suffer from particle aggregation, multi-stage process, complex setups and necessary nanofabrication. These increase process costs and time, reduce efficiency and limit dynamic range. Here, we achieve an unprecedented real-time nanoparticle separation (51–1500 nm) using a large-pore (2 μm) deterministic lateral displacement (DLD) device. No external force fields or nanofabrication are required. Instead, we investigated innate long-range electrostatic influences on nanoparticles within a fluid medium at different NaCl ionic concentrations. In this study we account for the electrostatic forces beyond Debye length and showed that they cannot be assumed as negligible especially for precise nanoparticle separation methods such as DLD. Our findings have enabled us to develop a model to simultaneously quantify and modulate the electrostatic force interactions between nanoparticle and micropore. By simply controlling buffer solutions, we achieve dynamic nanoparticle size separation on a single device with a rapid response time (<20 s) and an enlarged dynamic range (>1200%), outperforming standard benchtop centrifuge systems. This novel method and model combines device simplicity, isolation precision and dynamic flexibility, opening opportunities for high-throughput applications in nano-separation for industrial and biological applications.

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          Most cited references34

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          Continuous particle separation through deterministic lateral displacement.

          We report on a microfluidic particle-separation device that makes use of the asymmetric bifurcation of laminar flow around obstacles. A particle chooses its path deterministically on the basis of its size. All particles of a given size follow equivalent migration paths, leading to high resolution. The microspheres of 0.8, 0.9, and 1.0 micrometers that were used to characterize the device were sorted in 40 seconds with a resolution of approximately 10 nanometers, which was better than the time and resolution of conventional flow techniques. Bacterial artificial chromosomes could be separated in 10 minutes with a resolution of approximately 12%.
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            Properties and emerging applications of self-assembled structures made from inorganic nanoparticles.

            Just as nanoparticles display properties that differ from those of bulk samples of the same material, ensembles of nanoparticles can have collective properties that are different to those displayed by individual nanoparticles and bulk samples. Self-assembly has emerged as a powerful technique for controlling the structure and properties of ensembles of inorganic nanoparticles. Here we review different strategies for nanoparticle self-assembly, the properties of self-assembled structures of nanoparticles, and potential applications of such structures. Many of these properties and possible applications rely on our ability to control the interactions between the electronic, magnetic and optical properties of the individual nanoparticles.
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              Applications of Nanoparticles in Biology

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                Author and article information

                Journal
                LCAHAM
                Lab on a Chip
                Lab Chip
                Royal Society of Chemistry (RSC)
                1473-0197
                1473-0189
                2016
                2016
                : 16
                : 1
                : 75-85
                Article
                10.1039/C5LC01051A
                26575003
                8c1a82b2-50af-409d-b2e4-e8cfda5dc364
                © 2016
                History

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