Steric hindrance effect of Schiff-base ligands on magnetic relaxation dynamics and emissive behavior of two dinuclear dysprosium complexes

The self-assembly reactions between mixed-ligand and tetrahydrate dysprosium acetate in the presence of mixed organic solvents lead to two structural similar dinuclear dysprosium complexes with composition formulas of Dy_(2)(L_1)_(2)(L_(2))_(2)(CH_(3)OH)_(2)·CH_(2)Cl_(2)·CH_(3)OH(1) and Dy_(2)(L_1)_(2)(L_(3))_(2)(CH_(3)OH)_(2)·CH_(3)CN(2),where L_1,L_(2) and L_(3) represent the deprotonated form of 4-tert-butyl-2-(7-methoxybenzo[d]oxazol-2-yl)phenol,(E)-1-(((3,5-di-tert-butyI-2-hydroxyphenyI)imino)methyl)naphthalen-2-ol and(E)-2,4-di-tertbutyl-6-((2-hydroxybenzylidene)amino)phenol.The tiny difference of the core structure of 1 and 2 is derived from the steric hindrance of Schiff base ligands L_(2) and L_(3).Dynamic magnetic measurements reveal that 1 and 2 show frequency-dependent out-of-phase alternating-current susceptibility signal peaks at different temperatures under zero dc field,diagnostic of single-molecule magnet behavior.The experimental derived energy barrier to magnetization reversal for 1 and 2 is 108(1),47(2) and 33(3) K.Ab initio CASSCF calculations performed on 1 and 2 suggest that the origin of the difference in magnetic properties originates from the variation in the single-ion anisotropy that arises due to minor structural variation.Further,the equation to calculate the effective energy barrier for Dy_(2) proposed earlier is found to yield an excellent agreement with the experimental results.Solid state fluorescence measurements performed on 1 and 2 demonstrate that both exhibit two ligands centered components of fluorescent emissive,in addition,with different emitting colors and chromaticity coordinates.The discrepancy of fluorescence and single molecule magnet behavior showed by 1 and 2 can be attributed to the steric hindrance effect of Schiff base ligands.

Reinforcement learning building control approach harnessing imitation learning

Reinforcement learning(RL)has shown significant success in sequential decision making in fields like autonomous vehicles,robotics,marketing and gaming industries.This success has attracted the attention to the RL control approach for building energy systems which are becoming complicated due to the need to optimize for multiple,potentially conflicting,goals like occupant comfort,energy use and grid interactivity.However,for real world applications,RL has several drawbacks like requiring large training data and time,and unstable control behavior during the early exploration process making it infeasible for an application directly to building control tasks.To address these issues,an imitation learning approach is utilized herein where the RL agents starts with a policy transferred from accepted rule based policies and heuristic policies.This approach is successful in reducing the training time,preventing the unstable early exploration behavior and improving upon an accepted rule-based policy-all of these make RL a more practical control approach for real world applications in the domain of building controls.