Separation of cells is a critical process for studying cell properties, disease diagnostics, and therapeutics. Cell sorting by acoustic waves offers a means to separate cells on the basis of their size and physical properties in a label-free, contactless, and biocompatible manner. The separation sensitivity and efficiency of currently available acousticbased approaches, however, are limited, thereby restricting their widespread application in research and health diagnostics. In this work, we introduce a unique configuration of tilted-angle standing surface acoustic waves (taSSAW), which are oriented at an optimally designed inclination to the flow direction in the microfluidic channel. We demonstrate that this design significantly improves the efficiency and sensitivity of acoustic separation techniques. To optimize our device design, we carried out systematic simulations of cell trajectories, matching closely with experimental results. Using numerically optimized design of taSSAW, we successfully separated 2-and 10-μm-diameter polystyrene beads with a separation efficiency of ∼99%, and separated 7.3-and 9.9-μm-polystyrene beads with an efficiency of ∼97%. We illustrate that taSSAW is capable of effectively separating particles-cells of approximately the same size and density but different compressibility. Finally, we demonstrate the effectiveness of the present technique for biological-biomedical applications by sorting MCF-7 human breast cancer cells from nonmalignant leukocytes, while preserving the integrity of the separated cells. The method introduced here thus offers a unique route for separating circulating tumor cells, and for label-free cell separation with potential applications in biological research, disease diagnostics, and clinical practice.particle separation | microfluidics | cancer cell separation | acoustofluidics | tilt-angle optimization

induced scission measurements, imaging with AFM and TEM, and analysis of data. A.J.L. performed FRET measurements and analysis of the data. X.Z., T.C.-T., and Y.C. performed solution X-ray scattering, and analysis of the data. A.J.L. and Y.C. conceptualized nanoribbon thread processing and Y.C. and M.G. prepared nanoribbon threads. M.G. performed tensile testing of nanoribbon threads and analysis of the data. T.C.-T. and Y.C. performed X-ray scattering of solid-state nanoribbon threads and analysis of the data. J.

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The ability to measure the bulk dynamic behavior of soft materials with combined time-and frequency-resolution is instrumental for improving our fundamental understanding of connections between the microstructural dynamics and the macroscopic mechanical response. Current state-ofthe-art techniques are often limited by a compromise between resolution in the time and frequency domain, mainly due to the use of elementary input signals that have not been designed for fast time-evolving systems such as materials undergoing gelation, curing or self-healing. In this work, we develop an optimized and robust excitation signal for time-resolved mechanical spectroscopy through the introduction of joint frequency-and amplitude-modulated exponential chirps. Inspired by the biosonar signals of bats and dolphins, we optimize the signal profile to maximize the signal-to-noise ratio while minimizing spectral leakage with a carefully-designed modulation of the envelope of the chirp, obtained using a cosine tapered window function. A combined experimental and numerical investigation reveals that there exists an optimal range of window profiles (around 10% of the total signal length) that minimizes the error with respect to standard single frequency sweep techniques. The minimum error is set by the noise floor of the instrument, suggesting that the accuracy of an optimally windowed chirp (OWCh) sequence is directly comparable to that achievable with a standard frequency sweep, while the acquisition time can be reduced by up to two orders of magnitude, for comparable spectral content. Finally, we demonstrate the ability of this optimized signal to provide time-and frequency-resolved rheometric data by studying the fast gelation process of an acid-induced protein gel using repeated OWCh pulse sequences. The use of optimally windowed chirps enables a robust time-resolved rheological characterization of a wide range of soft materials undergoing rapid mutation and has the potential to become an invaluable tool for researchers across different disciplines.

Colloidal gels result from the aggregation of Brownian particles suspended in a solvent. Gelation is induced by attractive interactions between individual particles that drive the formation of clusters, which in turn aggregate to form a space-spanning structure. We study this process in aluminosilicate colloidal gels through time-resolved structural and mechanical spectroscopy. Using the time–connectivity superposition principle a series of rapidly acquired linear viscoelastic spectra, measured throughout the gelation process by applying an exponential chirp protocol, are rescaled onto a universal master curve that spans over eight orders of magnitude in reduced frequency. This analysis reveals that the underlying relaxation time spectrum of the colloidal gel is symmetric in time with power-law tails characterized by a single exponent that is set at the gel point. The microstructural mechanical network has a dual character; at short length scales and fast times it appears glassy, whereas at longer times and larger scales it is gel-like. These results can be captured by a simple three-parameter constitutive model and demonstrate that the microstructure of a mature colloidal gel bears the residual skeleton of the original sample-spanning network that is created at the gel point. Our conclusions are confirmed by applying the same technique to another well-known colloidal gel system composed of attractive silica nanoparticles. The results illustrate the power of the time–connectivity superposition principle for this class of soft glassy materials and provide a compact description for the dichotomous viscoelastic nature of weak colloidal gels.