This is a first attempt to use an averaged MSE error surface (obtained from the prediction of different patients' tumor trajectories) to determine the parameters of a generalized neural network. This network could be deployed as a plug-and-play predictor for tumor trajectory during treatment delivery, eliminating the need for optimizing individual networks with pretreatment patient data.

No abstract

Nanoparticles are highly versatile and exhibit broad applications in tuning material properties. Herein, we show through molecular dynamics simulations the possibility of a nanometer water pump, driven by the motion of nanoparticles (NPs) on a membrane surface. Surprisingly, considerable net water flux can be induced through a carbon nanotube (CNT) that is perpendicular to the NP motion. The water transport can occur in a highly controllable fashion, not only by using a single NP with different forces, but also by varying the CNT length or the NP number. Specifically, for a single NP, the water flow and flux are found to increase linearly with an increase in force, following the same behavior of NP velocity. Inversely, the water translocation time exhibits a linear decrease. We further revealed the unique relation between the water flow and occupancy divided by the translocation time. The CNT length can significantly screen the thermal fluctuation of an outside water reservoir, leading to an increase in the water flux and subsequent unidirectional transport. More interestingly, under moderate force, the water flow and flux demonstrate maximum behaviors with an increase in NP number, co-determined by the NP velocity and water occupancy. The maximum location shifts to the lower NP number region for a larger force. We also identify two CNT states that correspond to low water flow. Our results provide a significant new method to pump water molecules through a CNT channel, which is helpful for the design of controllable nanofluidic devices.

No abstract

Molecular motors offer promising applications in the fields of nanodevices and nanofluidics. It is thus highly relevant to study their practical operation processes in fluids. In this work, we adopted the torque approach based on quantum mechanical-calculated results to explicitly demonstrate that liquids hinder the rotation of a cogwheel-gearing system consisting of two nonpolar hexaethynyl-benzene molecules stacked on graphene with π−π bonding. For nine common organic solvents (some of which can be viewed as small models of lubricants)acetic acid, propylene carbonate, benzene, ethyl acetate, ethanol, tetrahydrofuran, acetone, acetonitrile, and n-hexane−torque profiles reveal a counterintuitive increasing hindrance effect with decreasing solvent viscosity. Through a further analysis by the reduced density gradient method, we find that noncovalent interactions, that is, dispersion forces between the solvents and gears, dominate in obstructing nonpolar gear rotation transfer in the solvents of lower viscosity; our torque approach thus predicts a significant solvent effect on molecular motors. This study shows that the torque approach can help better understand the mechanisms of molecular rotors working in a realistic liquid medium and guide the design of effective molecular motors for viscosity probes or pumping fluids, for example.