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Concepts for standard and proposed optical detection methods. (a) The OBDT requires a focused beam carefully aligned to a cantilever and the center of a segmented photo-diode. (b) In the NANOBE technique, a broad beam illuminates the whole cantilever array generating a diffraction pattern projected onto a CMOS detector. The intensity profile of the diffraction pattern is insensitive to misalignments but sensitive to the details of the cantilever bending.
Intensity map I(x), where x is pixel position along cantilever direction on the detector, for a cantilever as a function of different relevant variables. Insets are vertical cross-sections of the intensity map showing the intensity profiles for specific abscissa values. The horizontal axes are (a) Fresnel number , where L is the cantilever length, and z the distance from the cantilever at which the intensity is measured, (b) c 1 the cantilever tilt, (c) c 2 the cantilever curvature, and (d) c 3 the cubic bending.
Experimental measurements using NANOBE method. (a) Setup schematics: A cantilever array is illuminated with a broad laser beam through a cubic beam-splitter and the reflected light captured by a lens and CCD. (b) A single cantilever is pushed with a glass tip mounted over a calibrated piezoelectric actuator. (c) Diffraction images ( ) of the pushed cantilever (top pattern) are shorter than the patterns from the relaxed cantilever by a factor of . This corresponds to a lensing effect from the curved mirror formed by the cantilever. (d) The experimentally measured pattern size changes linearly with the cantilever tip displacement closely overlapping the model. (e) The curvatures of six cantilevers as a function of time. Cantilevers functionalized with MHA (top) and HDT (bottom) have different static bending and different sensitivity to pH changes.
The cantilever bending caused by small temperature changes are easily resolvable. The temperature protocol was set to , and . The light trace corresponds to the raw data and the darker trace is a 10 point moving average. The right hand side graph shows a kernel density estimation of raw data demonstrating that the measured curvatures feature perfectly distinguishable distributions.
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