The critical Casimir force provides a thermodynamic analogue of the quantum mechanical Casimir force that arises from the confinement of electromagnetic field fluctuations. In its thermodynamic analogue, two surfaces immersed in a critical solvent mixture attract each other due to confinement of solvent concentration fluctuations. Here, we demonstrate the active assembly control of colloidal equilibrium phases using critical Casimir forces. We guide colloidal particles into analogues of molecular liquid and solid phases via exquisite control over their interactions. By measuring the critical Casimir pair potential directly from density fluctuations in the colloidal gas, we obtain insight into liquefaction at small scales. We apply the van der Waals model of molecular liquefaction and show that the colloidal gas-liquid condensation is accurately described by the van der Waals theory, even on the scale of a few particles. These results open up new possibilities in the active assembly control of micro and nanostructures.

Directing self-assembly processes out-of-equilibrium to yield kinetically trapped materials with well-defined dimensions remains a considerable challenge. Kinetically controlled assembly of self-synthesizing peptide-functionalized macrocycles through a nucleation-growth mechanism is reported. Spontaneous fiber formation in this system is effectively shut down as most of the material is diverted into metastable non-assembling trimeric and tetrameric macrocycles. However, upon adding seeds to this mixture, well-defined fibers with controllable lengths and narrow polydispersities are obtained. This seeded growth strategy also allows access to supramolecular triblock copolymers. The resulting noncovalent assemblies can be further stabilized through covalent capture. Taken together, these results show that self-synthesizing materials, through their interplay between dynamic covalent bonds and noncovalent interactions, are uniquely suited for out-of-equilibrium self-assembly.

We report Monte Carlo simulations of phase behavior of colloidal suspensions with near-critical binary solvents using effective pair potentials from experiments. At off-critical solvent composition, the calculated phase diagram agrees well with measurements of the experimental system, indicating that many-body effects are limited. Close to the critical composition, however, agreement between experiment and simulation becomes poorer, signaling the increased importance of many-body effects. Both at and off the critical solvent concentration, the colloidal phase diagram is qualitatively similar to those of molecular systems and obeys the principle of corresponding states with one striking difference: it occurs in a narrow temperature interval of <1 • C below the solvent phase separation temperature.

Channel estimation plays a critical role in the system performance of wireless networks. In addition, deep learning has demonstrated significant improvements in enhancing the communication reliability and reducing the computational complexity of 5G-and-beyond networks. Even though least squares (LS) estimation is popularly used to obtain channel estimates due to its low cost without any prior statistical information regarding the channel, this method has relatively high estimation error. This paper proposes a new channel estimation architecture with the assistance of deep learning in order to improve the channel estimation obtained by the LS approach. Our goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile for simulations in 5G-and-beyond networks under the level of mobility expressed by the Doppler effects. The system model is constructed for an arbitrary number of transceiver antennas, while the machine learning module is generalized in the sense that an arbitrary neural network architecture can be exploited. Numerical results demonstrate the superiority of the proposed deep learning-based channel estimation framework over the other traditional channel estimation methods popularly used in previous works. In addition, bidirectional long short-term memory offers the best channel estimation quality and the lowest bit error ratio among the considered artificial neural network architectures.

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