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Accelerated discovery of CO2 electrocatalysts using active machine learning 期刊论文
NATURE, 2020, 581 (7807) : 178-+
作者:  Lan, Jun;  Ge, Jiwan;  Yu, Jinfang;  Shan, Sisi;  Zhou, Huan;  Fan, Shilong;  Zhang, Qi;  Shi, Xuanling;  Wang, Qisheng;  Zhang, Linqi;  Wang, Xinquan
收藏  |  浏览/下载:128/0  |  提交时间:2020/07/03

The rapid increase in global energy demand and the need to replace carbon dioxide (CO2)-emitting fossil fuels with renewable sources have driven interest in chemical storage of intermittent solar and wind energy(1,2). Particularly attractive is the electrochemical reduction of CO2 to chemical feedstocks, which uses both CO2 and renewable energy(3-8). Copper has been the predominant electrocatalyst for this reaction when aiming for more valuable multi-carbon products(9-16), and process improvements have been particularly notable when targeting ethylene. However, the energy efficiency and productivity (current density) achieved so far still fall below the values required to produce ethylene at cost-competitive prices. Here we describe Cu-Al electrocatalysts, identified using density functional theory calculations in combination with active machine learning, that efficiently reduce CO2 to ethylene with the highest Faradaic efficiency reported so far. This Faradaic efficiency of over 80 per cent (compared to about 66 per cent for pure Cu) is achieved at a current density of 400 milliamperes per square centimetre (at 1.5 volts versus a reversible hydrogen electrode) and a cathodic-side (half-cell) ethylene power conversion efficiency of 55 +/- 2 per cent at 150 milliamperes per square centimetre. We perform computational studies that suggest that the Cu-Al alloys provide multiple sites and surface orientations with near-optimal CO binding for both efficient and selective CO2 reduction(17). Furthermore, in situ X-ray absorption measurements reveal that Cu and Al enable a favourable Cu coordination environment that enhances C-C dimerization. These findings illustrate the value of computation and machine learning in guiding the experimental exploration of multi-metallic systems that go beyond the limitations of conventional single-metal electrocatalysts.


  
Local and global consequences of reward-evoked striatal dopamine release 期刊论文
NATURE, 2020, 580 (7802) : 239-+
作者:  Wagner, Felix R.;  Dienemann, Christian;  Wang, Haibo;  Stuetzer, Alexandra;  Tegunov, Dimitry;  Urlaub, Henning;  Cramer, Patrick
收藏  |  浏览/下载:27/0  |  提交时间:2020/07/03

The neurotransmitter dopamine is required for the reinforcement of actions by rewarding stimuli(1). Neuroscientists have tried to define the functions of dopamine in concise conceptual terms(2), but the practical implications of dopamine release depend on its diverse brain-wide consequences. Although molecular and cellular effects of dopaminergic signalling have been extensively studied(3), the effects of dopamine on larger-scale neural activity profiles are less well-understood. Here we combine dynamic dopamine-sensitive molecular imaging(4) and functional magnetic resonance imaging to determine how striatal dopamine release shapes local and global responses to rewarding stimulation in rat brains. We find that dopamine consistently alters the duration, but not the magnitude, of stimulus responses across much of the striatum, via quantifiable postsynaptic effects that vary across subregions. Striatal dopamine release also potentiates a network of distal responses, which we delineate using neurochemically dependent functional connectivity analyses. Hot spots of dopaminergic drive notably include cortical regions that are associated with both limbic and motor function. Our results reveal distinct neuromodulatory actions of striatal dopamine that extend well beyond its sites of peak release, and that result in enhanced activation of remote neural populations necessary for the performance of motivated actions. Our findings also suggest brain-wide biomarkers of dopaminergic function and could provide a basis for the improved interpretation of neuroimaging results that are relevant to learning and addiction.


Molecular and functional magnetic resonance imaging in the rat reveals distinct neuromodulatory effects of striatal dopamine that extend beyond peak release sites and activate remote neural populations necessary for performing motivated actions.


  
Machine learning and artificial intelligence to aid climate change research and preparedness 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2019, 14 (12)
作者:  Huntingford, Chris;  Jeffers, Elizabeth S.;  Bonsall, Michael B.;  Christensen, Hannah M.;  Lees, Thomas;  Yang, Hui
收藏  |  浏览/下载:40/0  |  提交时间:2020/02/17
climate change  global warming  extreme weather  drought  artificial intelligence  machine learning  climate simulations  
Evaluation and machine learning improvement of global hydrological model-based flood simulations 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2019, 14 (11)
作者:  Yang, Tao;  Sun, Fubao;  Gentine, Pierre;  Liu, Wenbin;  Wang, Hong;  Yin, Jiabo;  Du, Muye;  Liu, Changming
收藏  |  浏览/下载:16/0  |  提交时间:2020/02/17
flood simulation  machine learning  global hydrological model  long short-term memory  
Detecting global urban expansion over the last three decades using a fully convolutional network 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2019, 14 (3)
作者:  He, Chunyang;  Liu, Zhifeng;  Gou, Siyuan;  Zhang, Qiaofeng;  Zhang, Jinshui;  Xu, Linlin
收藏  |  浏览/下载:11/0  |  提交时间:2019/04/09
fully convolutional network  global urban expansion  deep learning  nighttime light data  vegetation index  land surface temperature  
Faith and Rights. A participatory global learning evaluation with Digni members and partner organizations 科技报告
来源:Center for International Climate and Environmental Research-Oslo (CICERO). 出版年: 2015
作者:  Døhlie, Elsa;  Meer, Shamim;  Haugen, Hans Morten
收藏  |  浏览/下载:8/0  |  提交时间:2019/04/05
digni member's  church  missionary organisations  global learning