So far, single-molecule imaging has predominantly relied on fluorescence detection. We imaged single nonfluorescent azo dye molecules in room-temperature glycerol by the refractive effect of the heat that they release in their environment upon intense illumination. This photothermal technique provides contrast for the absorbing objects only, irrespective of scattering by defects or roughness, with a signal-to-noise ratio of ~10 for a single molecule in an integration time of 300 milliseconds. In the absence of oxygen, virtually no bleaching event was observed, even after more than 10 minutes of illumination. In a solution saturated with oxygen, the average bleaching time was of the order of 1 minute. No blinking was observed in the absorption signal. On the basis of bleaching steps, we obtained an average absorption cross section of 4 angstroms(2) for a single chromophore.

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We combine ultrafast pump-probe spectroscopy with optical trapping to study homogeneous damping of the acoustic vibrations of single gold nanospheres (80 nm diameter) and nanorods (25 nm diameter by 60 nm length) in water. We find a significant particle-to-particle variation in damping times. Our results indicate that vibrational damping occurs not only by dissipation into the liquid, but also by damping mechanisms intrinsic to the particle. Our experiment opens the study of mechanisms of intrinsic mechanical dissipation in metals at frequencies 1-1000 GHz, a range that has been difficult to access thus far.

We present the first quantitative measurements of the torque exerted on a single gold nanorod in a polarized three-dimensional optical trap. We determined the torque both by observing the time-averaged orientation distribution and by measuring the dynamics of the rotational brownian fluctuations. The measurements are in good agreement with calculations, where the temperature profile around the hot nanorod gives rise to a reduced, effective viscosity. The maximum torque on a 60  nm×25  nm nanorod was 100  pN·nm, large enough to address single-molecule processes in soft and biological matter.

Despite extensive scrutiny of the myosin superfamily, the lack of high-resolution structures of actin-bound states has prevented a complete description of its mechanochemical cycle and limited insight into how sequence and structural diversification of the motor domain gives rise to specialized functional properties. Here we present cryo-EM structures of the unique minus-end directed myosin VI motor domain in rigor (4.6 Å) and Mg-ADP (5.5 Å) states bound to F-actin. Comparison to the myosin IIC-F-actin rigor complex reveals an almost complete lack of conservation of residues at the actin-myosin interface despite preservation of the primary sequence regions composing it, suggesting an evolutionary path for motor specialization. Additionally, analysis of the transition from ADP to rigor provides a structural rationale for force sensitivity in this step of the mechanochemical cycle. Finally, we observe reciprocal rearrangements in actin and myosin accompanying the transition between these states, supporting a role for actin structural plasticity during force generation by myosin VI.

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Gold vibrations: A new elastic (stretching) mode, appearing in individual dumbbells of gold nanospheres at 5–7 GHz (see figure), is a function of the contact area. This can be used to estimate the contact area between the particles, which plays an important role in the local enhancement of electromagnetic fields in such nanoantenna structures.

Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such parameters are difficult to determine from microscopic information. Seldom is this challenge more apparent than in active matter, where the hydrodynamic parameters are in fact fields that encode the distribution of energy-injecting microscopic components. Here, we use active nematics to demonstrate that neural networks can map out the spatiotemporal variation of multiple hydrodynamic parameters and forecast the chaotic dynamics of these systems. We analyze biofilament/molecular-motor experiments with microtubule/kinesin and actin/myosin complexes as computer vision problems. Our algorithms can determine how activity and elastic moduli change as a function of space and time, as well as adenosine triphosphate (ATP) or motor concentration. The only input needed is the orientation of the biofilaments and not the coupled velocity field which is harder to access in experiments. We can also forecast the evolution of these chaotic many-body systems solely from image sequences of their past using a combination of autoencoders and recurrent neural networks with residual architecture. In realistic experimental setups for which the initial conditions are not perfectly known, our physics-inspired machine-learning algorithms can surpass deterministic simulations. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems, even in the absence of knowledge of the underlying dynamics.