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| 1 | A techno-economic and life cycle assessment for the production of green methanol from CO_(2): catalyst and process bottlenecks显示文摘The success of catalytic schemes for the large-scale valorization of CO_(2) does not only depend on the development of active,selective and stable catalytic materials but also on the overall process design.Here we present a multidisciplinary study(from catalyst to plant and techno-economic/lifecycle analysis)for the production of green methanol from renewable H2 and CO_(2).We combine an in-depth kinetic analysis of one of the most promising recently reported methanol-synthesis catalysts(InCo)with a thorough process simulation and techno-economic assessment.We then perform a life cycle assessment of the simulated process to gauge the real environmental impact of green methanol production from CO_(2).Our results indicate that up to 1.75 ton of CO_(2) can be abated per ton of produced methanol only if renewable energy is used to run the process,while the sensitivity analysis suggest that either rock-bottom H2 prices(1.5$kg1)or severe CO_(2) taxation(300$per ton)are needed for a profitable methanol plant.Besides,we herein highlight and analyze some critical bottlenecks of the process.Especial attention has been paid to the contribution of H2 to the overall plant costs,CH4 trace formation,and purity and costs of raw gases.In addition to providing important information for policy makers and industrialists,directions for catalyst(and therefore process)improvements are outlined. | Tomas Cordero-Lanzac Adrian Ramirez Alberto Navajas Lieven Gevers Sirio Brunialti Luis MGandía Andrés T.Aguayo S.Mani Sarathy Jorge Gascon | 2022 | Journal of Energy Chemistry2022，31，5： | 3 |
| 2 | Effects of compressive force,particle size and moisture content on mechanical properties of biomass pellets from grasses显示文摘 | S.Mani L.G.Tabil | | 0，，07： | 1 |
| 3 | 查看详情显示文摘 | A.M.Bulgaru S.Mani S.Goel | | 0，，： | 1 |
| 4 | Predicting entropy and heat capacity of hydrocarbons using machine learning显示文摘Chemical substances are essential in all aspects of human life,and understanding their properties is essential for developing chemical systems.The properties of chemical species can be accurately obtained by experiments or ab initio computational calculations;however,these are time-consuming and costly.In this work,machine learning models(ML)for estimating entropy,S,and constant pressure heat capacity,Cp,at 298.15 K,are developed for alkanes,alkenes,and alkynes.The training data for entropy and heat capacity are collected from the literature.Molecular descriptors generated using alvaDesc software are used as input features for the ML models.Support vector regression(SVR),v-support vector regression(v-SVR),and random forest regression(RFR)algorithms were trained with K-fold cross-validation on two levels.The first level assessed the models’performance,and the second level generated the final models.Between the three ML models chosen,SVR shows better performance on the test dataset.The SVR model was then compared against traditional Benson’s group additivity to illustrate the advantages of using the ML model.Finally,a sensitivity analysis is performed to find the most critical descriptors in the property estimations. | Mohammed N.Aldosari Kiran K.Yalamanchi Xin Gao S.Mani Sarathy | 2021 | Energy and AI2021，4，2： | 0 |
| 5 | Building partnerships in a complex academic research environment显示文摘Change in the information landscape has afforded librarians an opportunity to actively seek new skills,knowledge,and opportunities in order to effectively integrate expertise at the point of need; in particular,librarians have shifted from being information providers to embedded information creators,integrators,and innovators. Our ability to capitalize on the strengths of our specific institutional environments and respond to information needs is ever more important if we are to remain central to the work of our academic enterprise. This article describes how the University of North Carolina at Chapel Hill ' s Health Sciences Library(HSL) has sought opportunities and established successful partnerships that integrate library expertise aligning with the two core strategies of Carolina's new strategic framework Blueprint for Next: Of the Public,For the Public,and Innovation Made Fundamental.These efforts have resulted in initiatives that improve access to quality health care and health care information for North Carolinians,accelerate the campus' research enterprise,and demonstrate the importance of evidence-based care at UNC and globally. By understanding and identifying the needs of our various stakeholders,we have been able to progress with our understanding of what key problems need to be solved,what interventions need to be developed,and in what ways librarians can integrate expertise around information synthesis and critical thinking so that we are seen as valued partners in our complex academic environment. | Nandita S.Mani Diana McDuffee Francesca Allegri Christie Degener | 2018 | 中华医学图书情报杂志2018，27，1： | 0 |
| 6 | 在复杂的学术研究环境中构建合作伙伴关系显示文摘信息景观的变化给图书馆员提供了一个寻求新的技能、知识和机遇的机会,以便在需要时能够有效地整合专业知识;尤其是图书馆员已经从信息提供者转变为嵌入式信息创造者、集成者和创新者。如果我们想继续保持作为学术工作中心的地位,我们就必须具备利用自身具体制度环境优势的能力,并能够对信息需求做出迅速反应。介绍了北卡罗莱纳大学教堂山分校的医学图书馆如何寻求机会,并如何成功地建立将图书馆专业知识与卡罗莱纳新的战略框架蓝图的两个核心战略相结合的合作伙伴关系,即面向大众的大众战略以及基础创新战略。这些努力为北卡罗莱纳州带来了更优质的医疗服务和相关保健信息,加快了校园研究企业的发展,并展示了在北卡罗莱纳大学和全球范围以内循证护理学的重要性。通过了解和识别我们各个利益相关者的需求,我们已经能够了解我们需要解决哪些关键问题、需要采取什么样的干预以及图书馆员如何围绕信息合成和批判性思维整合专业知识,以便我们能够在复杂的学术环境中始终处于被认同的有价值的合作伙伴地位。 | Nandita S.Mani Diana McDuffee Francesca Allegri Christie Degener 王先林 | 2018 | 中华医学图书情报杂志2018，27，1： | 0 |
| 7 | WDP1轻型客运机车的开发及利用计算机模拟进行设计评估显示文摘印度铁路使用的WDM2型干线内燃机车,是一种客货两用机车。它是迄今为止印度在客运和货运中大量使用的主型机车,在最近的30年中一直工作得很出色。然而,在当今迅变的情况下,它的作用正逐渐减小。因为今天的要求是提高能量效率,并且优化机车设计,而WDM2型机车是作为在重载高粘着的货运设计和轻载高速客运设计之间的一种折衷方案而设计的,因此它并非是一种好的选择。设在勒克瑙的印度铁路的研究、设计和标准组织已设计了一种B0-B0机车,与WDM2型机车的客运能力相当,但油耗降低了15%~20%。文中阐述了该设计工作的思路,并说明了运用计算机模拟技术评估此机车设计方案。 | S.MANI 杨兵 | 1997 | 国外内燃机车1997，，12： | 0 |
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