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Revealing enigmatic mucus structures in the deep sea using DeepPIV 期刊论文
NATURE, 2020, 583 (7814) : 78-+
作者:  Nguyen, Ngoc Uyen Nhi;  Canseco, Diana C.;  Xiao, Feng;  Nakada, Yuji;  Li, Shujuan;  Lam, Nicholas T.;  Muralidhar, Shalini A.;  Savla, Jainy J.;  Hill, Joseph A.;  Le, Victor;  Zidan, Kareem A.;  El-Feky, Hamed W.;  Wang, Zhaoning;  Ahmed, Mahmoud Salama;  Hubbi, Maimon E.;  Menendez-Montes, Ivan
收藏  |  浏览/下载:13/0  |  提交时间:2020/06/09

Advanced deep-sea imaging tools yield insights into the structure and function of mucus filtration houses built by midwater giant larvaceans.


Many animals build complex structures to aid in their survival, but very few are built exclusively from materials that animals create (1,2). In the midwaters of the ocean, mucoid structures are readily secreted by numerous animals, and serve many vital functions(3,4). However, little is known about these mucoid structures owing to the challenges of observing them in the deep sea. Among these mucoid forms, the '  houses'  of larvaceans are marvels of nature(5), and in the ocean twilight zone giant larvaceans secrete and build mucus filtering structures that can reach diameters of more than 1 m(6). Here we describe in situ laser-imaging technology(7) that reconstructs three-dimensional models of mucus forms. The models provide high-resolution views of giant larvacean houses and elucidate the role that house structure has in food capture and predator avoidance. Now that tools exist to study mucus structures found throughout the ocean, we can shed light on some of nature'  s most complex forms.


  
Non-volatile electric control of spin-charge conversion in a SrTiO3 Rashba system 期刊论文
NATURE, 2020, 580 (7804) : 483-+
作者:  Collombet, Samuel;  Ranisavljevic, Noemie;  Nagano, Takashi;  Varnai, Csilla;  Shisode, Tarak;  Leung, Wing;  Piolot, Tristan;  Galupa, Rafael;  Borensztein, Maud;  Servant, Nicolas;  Fraser, Peter;  Ancelin, Katia;  Heard, Edith
收藏  |  浏览/下载:14/0  |  提交时间:2020/07/03

The polarization direction of a ferroelectric-like state can be used to control the conversion of spin currents into charge currents at the surface of strontium titanate, a non-magnetic oxide.


After 50 years of development, the technology of today'  s electronics is approaching its physical limits, with feature sizes smaller than 10 nanometres. It is also becoming clear that the ever-increasing power consumption of information and communication systems(1) needs to be contained. These two factors require the introduction of non-traditional materials and state variables. As recently highlighted(2), the remanence associated with collective switching in ferroic systems is an appealing way to reduce power consumption. A promising approach is spintronics, which relies on ferromagnets to provide non-volatility and to generate and detect spin currents(3). However, magnetization reversal by spin transfer torques(4) is a power-consuming process. This is driving research on multiferroics to achieve low-power electric-field control of magnetization(5), but practical materials are scarce and magnetoelectric switching remains difficult to control. Here we demonstrate an alternative strategy to achieve low-power spin detection, in a non-magnetic system. We harness the electric-field-induced ferroelectric-like state of strontium titanate (SrTiO3)(6-9) to manipulate the spin-orbit properties(10) of a two-dimensional electron gas(11), and efficiently convert spin currents into positive or negative charge currents, depending on the polarization direction. This non-volatile effect opens the way to the electric-field control of spin currents and to ultralow-power spintronics, in which non-volatility would be provided by ferroelectricity rather than by ferromagnetism.


  
Direct-bandgap emission from hexagonal Ge and SiGe alloys 期刊论文
NATURE, 2020, 580 (7802) : 205-+
作者:  Meiners, Thorsten;  Frolov, Timofey;  Rudd, Robert E.;  Dehm, Gerhard;  Liebscher, Christian H.
收藏  |  浏览/下载:28/0  |  提交时间:2020/07/03

Silicon crystallized in the usual cubic (diamond) lattice structure has dominated the electronics industry for more than half a century. However, cubic silicon (Si), germanium (Ge) and SiGe alloys are all indirect-bandgap semiconductors that cannot emit light efficiently. The goal(1) of achieving efficient light emission from group-IV materials in silicon technology has been elusive for decades(2-6). Here we demonstrate efficient light emission from direct-bandgap hexagonal Ge and SiGe alloys. We measure a sub-nanosecond, temperature-insensitive radiative recombination lifetime and observe an emission yield similar to that of direct-bandgap group-III-V semiconductors. Moreover, we demonstrate that, by controlling the composition of the hexagonal SiGe alloy, the emission wavelength can be continuously tuned over a broad range, while preserving the direct bandgap. Our experimental findings are in excellent quantitative agreement with ab initio theory. Hexagonal SiGe embodies an ideal material system in which to combine electronic and optoelectronic functionalities on a single chip, opening the way towards integrated device concepts and information-processing technologies.


A hexagonal (rather than cubic) alloy of silicon and germanium that has a direct (rather than indirect) bandgap emits light efficiently across a range of wavelengths, enabling electronic and optoelectronic functionalities to be combined on a single chip.


  
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).


  
Symmetry 2017- The First International Conference on Symmetry 会议
Barcelona, Spain, 会议类型: Conference;Exhibition;Seminar, 2017