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On-device lead sequestration for perovskite solar cells 期刊论文
NATURE, 2020, 578 (7796) : 555-+
作者:  Fruchart, Michel;  Zhou, Yujie;  Vitelli, Vincenzo
收藏  |  浏览/下载:30/0  |  提交时间:2020/07/03

Perovskite solar cells, as an emerging high-efficiency and low-cost photovoltaic technology(1-6), face obstacles on their way towards commercialization. Substantial improvements have been made to device stability(7-10), but potential issues with lead toxicity and leaching from devices remain relatively unexplored(11-16). The potential for lead leakage could be perceived as an environmental and public health risk when using perovskite solar cells in building-integrated photovoltaics(17-23). Here we present a chemical approach for on-device sequestration of more than 96 per cent of lead leakage caused by severe device damage. A coating of lead-absorbing material is applied to the front and back sides of the device stack. On the glass side of the front transparent conducting electrode, we use a transparent lead-absorbing molecular film containing phosphonic acid groups that bind strongly to lead. On the back (metal) electrode side, we place a polymer film blended with lead-chelating agents between the metal electrode and a standard photovoltaic packing film. The lead-absorbing films on both sides swell to absorb the lead, rather than dissolve, when subjected to water soaking, thus retaining structural integrity for easy collection of lead after damage.


Using lead-absorbing materials to coat the front and back of perovskite solar cells can prevent lead leaching from damaged devices, without affecting the device performance or long-term operation stability.


  
Classification with a disordered dopantatom network in silicon 期刊论文
NATURE, 2020, 577 (7790) : 341-+
作者:  Vagnozzi, Ronald J.;  Maillet, Marjorie;  Sargent, Michelle A.;  Khalil, Hadi;  Johansen, Anne Katrine Z.;  Schwanekamp, Jennifer A.;  York, Allen J.;  Huang, Vincent;  Nahrendorf, Matthias;  Sadayappan, Sakthivel;  Molkentin, Jeffery D.
收藏  |  浏览/下载:23/0  |  提交时间:2020/07/03

Classification is an important task at which both biological and artificial neural networks excel(1,2). In machine learning, nonlinear projection into a high-dimensional feature space can make data linearly separable(3,4), simplifying the classification of complex features. Such nonlinear projections are computationally expensive in conventional computers. A promising approach is to exploit physical materials systems that perform this nonlinear projection intrinsically, because of their high computational density(5), inherent parallelism and energy efficiency(6,7). However, existing approaches either rely on the systems'  time dynamics, which requires sequential data processing and therefore hinders parallel computation(5,6,8), or employ large materials systems that are difficult to scale up(7). Here we use a parallel, nanoscale approach inspired by filters in the brain(1) and artificial neural networks(2) to perform nonlinear classification and feature extraction. We exploit the nonlinearity of hopping conduction(9-11) through an electrically tunable network of boron dopant atoms in silicon, reconfiguring the network through artificial evolution to realize different computational functions. We first solve the canonical two-input binary classification problem, realizing all Boolean logic gates(12) up to room temperature, demonstrating nonlinear classification with the nanomaterial system. We then evolve our dopant network to realize feature filters(2) that can perform four-input binary classification on the Modified National Institute of Standards and Technology handwritten digit database. Implementation of our material-based filters substantially improves the classification accuracy over that of a linear classifier directly applied to the original data(13). Our results establish a paradigm of silicon-based electronics for smallfootprint and energy-efficient computation(14).


  
A role for optics in AI hardware 期刊论文
NATURE, 2019, 569 (7755) : 199-200
作者:  Burr, Geoffrey W.
收藏  |  浏览/下载:0/0  |  提交时间:2019/11/27