| Posted: July 31, 2007 |
Nanoparticle pH meters |
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(Nanowerk News) Having the ability to measure pH in a tissue without the need for a biopsy could provide clinicians with a rapid method for determining if a suspicious growth is malignant. Two novel nanoparticles raise the distinct possibility that making such measurements could soon be reality.
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At Denmark’s Risø National Laboratory, Anne Marie Scharff-Poulsen, Ph.D., and her colleagues have created a polymer nanoparticle containing two fluorescent dyes. One of the dyes is sensitive to pH changes, while the other is not, and both are chemically attached to the polymer chains that make up the nanoparticle. Using the two dyes provides an internal standard that increases the accuracy and sensitivity of pH measurements – a change in the relative fluorescent emissions of the two dye molecules provides an absolute measurement of pH.
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The investigators demonstrated that this nanosensor is capable of measuring pH from 5.8 to 7.2. This range is well within limits relevant for cancer detection or for use in laboratory studies aimed at better understanding cancer cell biology. The researchers published their work in the journal Chemistry of Materials ("Synthesis and Characterization of Ratiometric, pH Sensing Nanoparticles with Covalently Attached Fluorescent Dyes").
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In a second report, published in the journal Nano Letters, Naomi Halas, Ph.D., and her collaborators at Rice University describe their use of gold-coated silica nanoshells as miniature pH meters. To form their pH meter, the investigators coated the nanoshells with the pH-sensitive molecule para-mercaptobenzoic acid (pMBA).
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When placed in solutions of varying acidity and illuminated, the nanoshell-molecule device provides small but easily detectable changes in the properties of the scattered light. Software that the team developed decodes the light-scattering data, yielding a direct measurement of the pH of the nanodevice's local environment to remarkably high accuracy. Inspired by techniques normally applied to image recognition, the team formulated an efficient statistical learning procedure to produce the device output, achieving an average accuracy of 0.1 pH units between pH 5.8 and 7.6.
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