Dataset Information

Hollow and highly diastereoselective face-rotating polyhedra constructed through rationally engineered facial units† † Electronic supplementary information (ESI) available. CCDC 2053589. For ESI and crystallographic data in CIF or other electronic format see DOI: 10.1039/d1sc03428f

ABSTRACT: Molecular face-rotating polyhedra (FRP) exhibit complex stereochemistry, rendering it challenging to manipulate their assembly in a stereoselective manner. In our previous work, stereocontrolled FRP were gained at the cost of losing the confined inner space, which hampers their host–guest interactions and potential applications. Through a rational design approach, herein we demonstrate the successful construction of hollow FRP with high diastereoselectivity. Whereas the [4 + 4] imine condensation of meta-formyl substituted C3h-symmetric TAT-m and C3-symmetric Tri-NH2 led to the formation of all feasible FRP-12 diastereoisomers; the para-substituted constitutional isomer, TAT-p, exclusively assembled into a pair of homo-directional enantiomeric FRP-13-CCCC/AAAA with a cavity size larger than 600 Å3. Detailed structural characterizations and theoretical investigations revealed the thermodynamic landscape of FRP assembly can be effectively shaped by modulating the van der Waals repulsive forces among the facial building blocks. Our work provided a novel strategy towards stereospecific assembly of pure organic cages, opening up new opportunities for further applications of these chiral materials. The rationally engineered facial units, TAT-m and TAT-p, resulted in distinct diastereoselectivity of face-rotating polyhedra (FRP).

SUBMITTER: Tang X

PROVIDER: S-EPMC8442696 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:This paper presents a new approach for evaluating and controlling expressive humanoid robotic faces using open-source computer vision and machine learning methods. Existing research in Human-Robot Interaction lacks flexible and simple tools that are scalable for evaluating and controlling various robotic faces; thus, our goal is to demonstrate the use of readily available AI-based solutions to support the process. We use a newly developed humanoid robot prototype intended for medical training applications as a case example. The approach automatically captures the robot's facial action units through a webcam during random motion, which are components traditionally used to describe facial muscle movements in humans. Instead of manipulating the actuators individually or training the robot to express specific emotions, we propose using action units as a means for controlling the robotic face, which enables a multitude of ways to generate dynamic motion, expressions, and behavior. The range of action units achieved by the robot is thus analyzed to discover its expressive capabilities and limitations and to develop a control model by correlating action units to actuation parameters. Because the approach is not dependent on specific facial attributes or actuation capabilities, it can be used for different designs and continuously inform the development process. In healthcare training applications, our goal is to establish a prerequisite of expressive capabilities of humanoid robots bounded by industrial and medical design constraints. Furthermore, to mediate human interpretation and thus enable decision-making based on observed cognitive, emotional, and expressive cues, our approach aims to find the minimum viable expressive capabilities of the robot without having to optimize for realism. The results from our case example demonstrate the flexibility and efficiency of the presented AI-based solutions to support the development of humanoid facial robots.

Dataset Information

Hollow and highly diastereoselective face-rotating polyhedra constructed through rationally engineered facial units† † Electronic supplementary information (ESI) available. CCDC 2053589. For ESI and crystallographic data in CIF or other electronic format see DOI: 10.1039/d1sc03428f

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets