We report on the continuous fine-scale tuning of band gaps over 0.4 eV and of the electrical conductivity of over 4 orders of magnitude in a series of highly crystalline binary alloys of two-dimensional electrically conducting metal–organic frameworks M3(HITP)2 (M = Co, Ni, Cu; HITP = 2,3,6,7,10,11-hexaiminotriphenylene). The isostructurality in the M3(HITP)2 series permits the direct synthesis of binary alloys (M x M′3–x )(HITP)2 (MM′ = CuNi, CoNi, and CoCu) with metal compositions precisely controlled by precursor ratios. We attribute the continuous tuning of both band gaps and electrical conductivity to changes in free-carrier concentrations and to subtle differences in the interlayer displacement or spacing, both of which are defined by metal substitution. The activation energy of (Co x Ni3–x )(HITP)2 alloys scales inversely with an increasing Ni percentage, confirming thermally activated bulk transport.
We report a one-pot synthesis of high-quality colloidal copper-doped cadmium selenide nanocrystals (Cu+:CdSe NCs) by injection of a mixture of copper iodide (CuI) and trioctylphosphine (TOP) into solutions containing preformed CdSe NCs. This method allows NC doping to be separated from nucleation and growth, thereby simultaneously achieving large size tunability, narrow size dispersion, and exclusively copper-based photoluminescence (PL). The copper doping level is affected by both the reaction time and the relative concentrations of the cadmium precursor, CuI, and TOP. A correlation is demonstrated between the copper dopant concentration and the intensities of the characteristic near-IR PL and midgap absorption bands, both associated with metal-to-ligand (conduction band) charge-transfer (MLCBCT) excitation of Cu+ dopants. Mechanistic studies reveal that Cu2–x Se NCs are easily formed as kinetic intermediates under reaction conditions involving substantial copper and that these NCs then act as a copper source for the subsequent formation of Cu+:CdSe NCs in the same reaction mixture. We also observe postsynthetic loss of copper from the doped NCs during shell growth or exposure to phosphines and amines, reflecting the high mobility of Cu+ ions in colloidal NCs.
The combination of deep neural network models and reinforcement learning algorithms can make it possible to learn policies for robotic behaviors that directly read in raw sensory inputs, such as camera images, effectively subsuming both estimation and control into one model. However, realworld applications of reinforcement learning must specify the goal of the task by means of a manually programmed reward function, which in practice requires either designing the very same perception pipeline that end-to-end reinforcement learning promises to avoid, or else instrumenting the environment with additional sensors to determine if the task has been performed successfully. In this paper, we propose an approach for removing the need for manual engineering of reward specifications by enabling a robot to learn from a modest number of examples of successful outcomes, followed by actively solicited queries, where the robot shows the user a state and asks for a label to determine whether that state represents successful completion of the task. While requesting labels for every single state would amount to asking the user to manually provide the reward signal, our method requires labels for only a tiny fraction of the states seen during training, making it an efficient and practical approach for learning skills without manually engineered rewards. We evaluate our method on real-world robotic manipulation tasks where the observations consist of images viewed by the robot's camera. In our experiments, our method effectively learns to arrange objects, place books, and drape cloth, directly from images and without any manually specified reward functions, and with only 1-4 hours of interaction with the real world. Videos of learned behavior are available at sites.
Me 3 tacn)] 2+ (Me 3 tacn = 1,4,7-trimethyl-1,4,7-triazacyclononane) produces trigonal tricopper complexes [(Me 3 tacnCu) 3 (HOTP)] 3+ (1) and [(Me 3 tacnCu) 3 (HITP)] 4+ (2) (HOTP, HITP = hexaoxy-and hexaimino-triphenylene, respectively). These trinuclear complexes are molecular models for spin exchange interactions in the two-dimensional conductive metal-organic frameworks (MOFs) copper hexaoxytriphenylene (Cu 3 HOTP 2) and copper hexaiminotriphenylene (Cu 3 HITP 2). Whereas complex 1 is isolated with HOTP 3bearing the same oxidation state as found in the oxy-bridged MOF, the triply oxidized HITP 3found in Cu 3 HITP 2 is unstable with respect to disproportionation in the molecular model. Indeed, magnetic measurements reveal ligandcentered radical character for 1 and a closed-shell structure for 2, in agreement with the redox state of the ligands. All neighboring spins are antiferromagnetically coupled in 1 and 2. These results help probe metal-ligand-metal interactions in conductive MOFs and provide potential inspiration for the synthesis of other two-dimensional materials with delocalized electrons. Figure 1. (a) Structural and chemical representation of typical 2D conductive MOFs, with depiction of the graphite-like honeycomb structure as well as HXTP (X = O, I) ligand-centered radical. The highlighted part illustrates the trinuclear metalligand monomeric unit modeled here as shown in (b). (c) Spin lattice of typical 2D conductive MOFs, with arrows showing randomized spin centers. (d) Spin structure of the monomeric unit depicted in (b), showing metal-and ligand-centered radicals.
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