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| 1 | The management of temperature during cardiopulmonary bypass: effect on neurophychological outcome显示文摘 | Nathan H J Munson J Wells G | 1995 | J Cardiovasc Surg1995，36，4： | 1 |
| 2 | Neuroprotective effect of mild hypothermia in patients undergoing coronary artery surgery with cardiopulmonary bypass: a randomized trial 显示文摘 | Nathan H J Wells GA Munson JL | 2001 | Circulation2001，104，1： | 1 |
| 3 | Neuropmtec- tire effect of mild hypothermia in patients undergoing coro- nary artery surgery with cardiopulmonary bypass: a ran- domized trial显示文摘 | Nathan HI Wells GA Munson JL et at | 2001 | Circulation2001，104，121： | 1 |
| 4 | Neuroprotective effect of mild hypothermia in patients undergoing coronary ar tery surgery with cardiopulmonary bypass: a randomized trial 显示文摘 | Nathan HJ Wells GA Munson JL | 2001 | Circulation2001，104，18： | 1 |
| 5 | Neuroprotective effect of mild hypothermia in patients undergoing coronary artery surgery with cardiopulmonary bypass:a randomized trial显示文摘 | Nathan HJ Wells GA Munson JL | 2001 | Circulation2001，104，121： | 1 |
| 6 | Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels显示文摘Irradiation increases the yield stress and embrittles light water reactor(LWR)pressure vessel steels.In this study,we demonstrate some of the potential benefits and risks of using machine learning models to predict irradiation hardening extrapolated to low flux,high fluence,extended life conditions.The machine learning training data included the Irradiation Variable for lower flux irradiations up to an intermediate fluence,plus the Belgian Reactor 2 and Advanced Test Reactor 1 for very high flux irradiations,up to very high fluence.Notably,the machine learning model predictions for the high fluence,intermediate flux Advanced Test Reactor 2 irradiations are superior to extrapolations of existing hardening models.The successful extrapolations showed that machine learning models are capable of capturing key intermediate flux effects at high fluence.Similar approaches,applied to expanded databases,could be used to predict hardening in LWRs under life-extension conditions. | Yu-chen Liu Henry Wu Tam Mayeshiba Benjamin Afflerbach Ryan Jacobs Josh Perry Jerit George Josh Cordell Jinyu Xia Hao Yuan Aren Lorenson Haotian Wu Matthew Parker Fenil Doshi Alexander Politowicz Linda Xiao Dane Morgan Peter Wells Nathan Almirall Takuya Yamamoto G.Robert Odette | 2022 | npj Computational Materials2022，，1： | 0 |
| 7 | Constructing a coplanar heterojunction through enhancedπ-πconjugation in g-C_(3)N_(4)for efficient solar-driven water splitting显示文摘Adjusting the electronic structure of graphitic carbon nitride(g-C_(3)N_(4))photocatalyst throughπ-πconju-gation is an effective method to achieve efficient photogenerated carrier separation.One key challenge ofπ-πconjugation control is to tune the degree of such conjugation without destroying the g-C_(3)N_(4)struc-ture.Herein we report a conceptual design that achieves a coplanar heterojunction by enhancing theπ-πconjugation via the doping of crystalline g-C_(3)N_(4)using a conjugated double bond ring molecule,1,3,5-benzenetriol,during calcination process.The selection of the dopant enables the facile creation of a unique coplanar heterojunction which not only retains the pristine network structure of g-C_(3)N_(4),but remarkably promotes separation and transfer of photogenerated carriers through the enhancedπ-conjugated endogenous electric field.As a result,the new g-C_(3)N_(4)photocatalyst efficiently photocatalyti-cally produces hydrogen from water under visible light irradiation with a high H 2 production rate up to 94.94μmol/h,and a notable external quantum efficiency of 16.4%at 420 nm. | Zihao Chen Ben Chong Nathan Wells Guidong Yang Lianzhou Wang | 2022 | Chinese Chemical Letters2022，33，5： | 0 |
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