1,033 research outputs found
Photoelectrochemical and photophysical studies of carbon nanotube and molybdenum disulfide based light harvesting devices
There is a critical need in utilizing solar radiation as a renewable energy source. While photovoltaic solar cells are widely used, much attention has been devoted in the past decade to developing nanotechnology for potential cost reduction and improved device efficiency and reliability. Low-dimensional materials offer unique physical properties which may be exploited for solar energy harvesting and conversion. Understanding their fundamental properties and developing relevant manufacturing strategies will thus pave the road toward high-performance, cost-effective, light-harvesting devices.
This thesis has investigated single-wall carbon nanotubes (SWCNTs) and molybdenum disulfide (MoS2) nanolayers for their light-harvesting ability in donor-acceptor systems. These materials were studied with three specific goals: (i) introducing innovative light-harvesting designs, (ii) understanding their fundamental photophysical and photoelectrochemical properties, and (iii) providing potential solutions to improve the system performance.
First, novel light-harvesting complexes were designed using semiconducting SWCNTs and cationic porphyrins as acceptors and donors, respectively. These complexes were assembled by synthetic DNA oligonucleotides that recognize porphyrins, while noncovalently functionalizing SWCNTs. The SWCNT-DNA-porphyrin hybrids were used to manufacture large-area thin films through solution-phase processing and membrane filtration methods. From extensive studies of optical absorption, emission, and photocurrents, new detailed insights on photo-processes were gained for photoelectrochemical conversion.
A regenerative donor-acceptor light-harvesting system was introduced and demonstrated to counteract photoinduced degradation of porphyrin molecules. The photo-damaged chromophores were dissociated from the complex by modulating the chemical environment, while DNA-SWCNTs were preserved. When fresh porphyrins were reintroduced and reassociated with DNA-SWCNTs, photocurrents were fully recovered. As proof-of-principle, A 50% increase in photocurrents was demonstrated through four successive regenerations within 90 minutes, compared to the complex without regeneration. Such dynamic strategy could improve the overall device efficiency and extend the operation lifetime.
Lastly, a novel solution-phase manufacturing process was developed to fabricate large-area two-dimensional MoS2 nanolayers for light harvesting applications. The MoS2 nanolayers were functionalized with 8 porphyrin species from 3 families to mitigate charge recombination by defects and small crystallites. A strong correlation between porphyrin species and photocurrents was observed, where interfacial porphyrins suppress charge recombination within MoS2 nanolayers, thus enhancing the photoelectrochemical performance of the devices. A photocurrent enhancement mechanism was proposed based on the energy difference between the valence band of MoS2 and highest occupied molecular orbital level of porphyrins.
Overall, the innovative designs and the scientific insights on photophysics and photoelectrochemical conversion in this thesis will form the basis for developing next-generation solar energy harvesting devices
Probing dynamics of dark energy with latest observations
We examine the validity of the CDM model, and probe for the dynamics
of dark energy using latest astronomical observations. Using the
diagnosis, we find that different kinds of observational data are in tension
within the CDM framework. We then allow for dynamics of dark energy
and investigate the constraint on dark energy parameters. We find that for two
different kinds of parametrisations of the equation of state parameter , a
combination of current data mildly favours an evolving , although the
significance is not sufficient for it to be supported by the Bayesian evidence.
A forecast of the DESI survey shows that the dynamics of dark energy could be
detected at confidence level, and will be decisively supported by the
Bayesian evidence, if the best fit model of derived from current data is
the true model.Comment: 4.5 pages, 3 figures, 1 table; references adde
Instance-weighted Central Similarity for Multi-label Image Retrieval
Deep hashing has been widely applied to large-scale image retrieval by
encoding high-dimensional data points into binary codes for efficient
retrieval. Compared with pairwise/triplet similarity based hash learning,
central similarity based hashing can more efficiently capture the global data
distribution. For multi-label image retrieval, however, previous methods only
use multiple hash centers with equal weights to generate one centroid as the
learning target, which ignores the relationship between the weights of hash
centers and the proportion of instance regions in the image. To address the
above issue, we propose a two-step alternative optimization approach,
Instance-weighted Central Similarity (ICS), to automatically learn the center
weight corresponding to a hash code. Firstly, we apply the maximum entropy
regularizer to prevent one hash center from dominating the loss function, and
compute the center weights via projection gradient descent. Secondly, we update
neural network parameters by standard back-propagation with fixed center
weights. More importantly, the learned center weights can well reflect the
proportion of foreground instances in the image. Our method achieves the
state-of-the-art performance on the image retrieval benchmarks, and especially
improves the mAP by 1.6%-6.4% on the MS COCO dataset.Comment: 10 pages, 6 figure
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