From Nanoscale Structure to Nanoscale Function (NS2NF)

从纳米级结构到纳米级功能（NS2NF）

• 批准号: EP/R029229/1
• 负责人: George Briggs
• 金额: $ 195.03万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FR029229%2F1
• 关键词: Nanoscale; Structure; Function; NS2NF

As we gain ever-greater control of materials on a very small scale, so a new world of possibilities opens up to be studied for their scientific interest and harnessed for their technological benefits. In science and technology nano often denotes tiny things, with dimensions measured in billionths of metres. At this scale structures have to be understood in terms of the positions of individual atoms and the chemical bonds between them. The flow of electricity can behave like waves, with the effects adding or subtracting like ripples on the surface of a pond into which two stones have been dropped a small distance apart. Electrons can behave like tiny magnets, and could provide very accurate timekeeping in a smartphone. Carbon nanotubes can vibrate like guitar strings, and just as the pitch of a note can be changed by a finger, so they can be sensitive to the touch of a single molecule. In all these effects, we need to understand how the function on the nanoscale relates to the structure on the nanoscale.This requires a comprehensive combination of scientific skills and methods. First, we have to be able to make the materials which we shall use. This is the realm of chemistry, but it also involves growth of new carbon materials such as graphene and single-walled carbon nanotubes. Second, we need to fabricate the tiny devices which we shall measure. Most commonly we use a beam of electrons to pattern the structures which we need, though there are plenty of other methods which we use as well. Third, we need to see what we have made, and know whether it corresponds to what we intended. For this we again use beams of electrons, but now in microscopes that can image how individual atoms are arranged. Fourth, we need to measure how what we have made functions, for example how electricity flows through it or how it can be made to vibrate. A significant new development in our laboratory is the use of machine learning for choosing what to measure next. We have set ourselves the goal that within five years the machine will decide what the next experiment should be to the standard of a second-year graduate student.The Platform Grant renewal 'From Nanoscale Structure to Nanoscale Function' will provide underpinning support for a remarkable team of researchers who bring together exactly the skills set which is needed for this kind of research. It builds on the success of the current Platform Grant 'Molecular Quantum Devices'. This grant has given crucial support to the team and to the development of their careers. The combination of skills, and the commitment to working towards shared goals, has empowered the team to make progress which would not have been possible otherwise. For example, our team's broad range of complementary skills were vital in allowing us to develop a method, now patented, for making nanogaps in graphene. This led to reproducible and stable methods of making molecular quantum devices, the core subject of that grant. The renewal of the Platform Grant will underpin other topics that also build on achievements of the current grant, and which require a similar set of skills to determine how function on the nanoscale depends on structure on the nanoscale.You can get a flavour of the research to be undertaken by the questions which motivate the researchers to be supported by the grant. Here is a selection. Can we extend quantum control to bigger things? Can molecular scale magnets be controlled by a current? How do molecules conduct electricity? How can we pass information between light and microwaves? How can we measure a thousand quantum devices in a single experiment? Are the atoms in our devices where we want them? Can computers decide what to measure next? As we make progress in questions like these, so we shall better understand how structure on the nanoscale gives rise to function on the nanoscale. And that understanding will in turn provide the basis for new discoveries and new technologies.

随着我们在非常小的范围内对材料获得越来越大的控制，一个新的可能性世界打开了，人们可以为了他们的科学兴趣而进行研究，并为他们的技术利益而利用。在科学和技术领域，纳米通常表示微小的东西，其尺寸以十亿分之一米为单位。在这个尺度上，必须根据单个原子的位置和它们之间的化学键来理解结构。电流的行为可以像波浪一样，其效果就像池塘表面的涟漪一样相加或相减，两块石头被扔进池塘，相隔很小的距离。电子的行为就像微小的磁铁，可以在智能手机上提供非常精确的计时。碳纳米管可以像吉他弦一样振动，就像音符的音调可以通过手指改变一样，因此它们可以对单个分子的触摸敏感。在所有这些效应中，我们需要了解纳米尺度上的功能与纳米尺度上的结构之间的关系。这需要科学技能和方法的综合结合。首先，我们必须能够制造我们将使用的材料。这是化学领域，但它也涉及新的碳材料的增长，如石墨烯和单壁碳纳米管。其次，我们需要制造我们将测量的微小设备。最常见的是，我们使用电子束来形成我们所需要的结构图案，尽管我们也使用了许多其他方法。第三，我们需要看到我们做了什么，并知道它是否符合我们的意图。为此，我们再次使用电子束，但现在在显微镜中，可以成像单个原子是如何排列的。第四，我们需要测量我们制造的东西是如何发挥作用的，例如，电流如何流过它，或者它如何被振动。我们实验室的一个重要新发展是使用机器学习来选择下一步要测量的内容。我们已经为自己设定了目标，在五年内，这台机器将决定下一步的实验应该是什么，应该达到二年级研究生的标准。平台授权续订“从纳米级结构到纳米级功能”将为一支出色的研究团队提供支持，他们恰好汇集了这类研究所需的技能集。它建立在目前平台GRANT‘分子量子器件’的成功基础上。这笔赠款为团队和他们的职业发展提供了至关重要的支持。技能的结合，以及努力实现共同目标的承诺，赋予了团队取得进展的能力，否则是不可能的。例如，我们团队广泛的互补技能对于我们开发一种方法至关重要，这种方法现在已经获得专利，用于在石墨烯中制造纳米薄膜。这导致了制造分子量子器件的可重复性和稳定性的方法，这是该拨款的核心主题。平台资助的续期将支持其他课题，这些课题也建立在当前拨款成果的基础上，这些课题需要一套类似的技能来确定纳米尺度上的功能如何依赖于纳米尺度上的结构。你可以通过激励研究人员获得拨款支持的问题来了解将进行的研究。这里有一些精选。我们能把量子控制扩展到更大的事物上吗？分子尺度的磁铁能被电流控制吗？分子是如何导电的？我们如何在光和微波之间传递信息？我们如何在一次实验中测量一千个量子设备？我们设备中的原子在我们想要的地方吗？计算机能决定下一步要测量什么吗？随着我们在这些问题上取得进展，我们将更好地理解纳米尺度上的结构如何产生纳米尺度上的功能。而这种理解将反过来为新发现和新技术提供基础。

项目成果

Charge-State Dependent Vibrational Relaxation in a Single-Molecule Junction.

• DOI: 10.1103/physrevlett.129.207702
• 发表时间: 2022-02
• 期刊: Physical review letters
• 影响因子: 8.6
• 作者: X. Bian;Zhixing Chen;Jakub K. Sowa;C. Evangeli;B. Limburg;J. Swett;J. Baugh;G. Briggs;H. Anderson;J. Mol;James O. Thomas

Genomics of carbon atomic chains

• DOI: 10.1016/j.carbon.2021.07.079
• 发表时间: 2021-08-09
• 期刊: CARBON
• 影响因子: 10.9
• 作者: Daaoub, Abdalghani;Lambert, Colin J.;Sadeghi, Hatef
• 通讯作者: Sadeghi, Hatef

Phase-Coherent Charge Transport through a Porphyrin Nanoribbon.

• DOI: 10.1021/jacs.3c02451
• 发表时间: 2023-07-19
• 期刊: Journal of the American Chemical Society
• 影响因子: 15
• 作者: Chen Z;Deng JR;Hou S;Bian X;Swett JL;Wu Q;Baugh J;Bogani L;Briggs GAD;Mol JA;Lambert CJ;Anderson HL;Thomas JO
• 通讯作者: Thomas JO

Identifying Pauli spin blockade using deep learning

使用深度学习识别泡利自旋封锁

• DOI: 10.21203/rs.3.rs-1340093/v1
• 发表时间: 2022
• 作者: Ares N

Promotion and suppression of single-molecule conductance by quantum interference in macrocyclic circuits

• DOI: 10.1016/j.matt.2021.08.016
• 发表时间: 2021-11-03
• 期刊: MATTER
• 影响因子: 18.9
• 作者: Chen, Honglian;Hou, Songjun;Stoddart, J. Fraser
• 通讯作者: Stoddart, J. Fraser