最强X射线激光轰击单个分子:制造出“迷你黑洞”

堪萨斯州立大学的研究人员惊奇地发现,当他们用世界上最强的X射线激光轰击单个分子时,出现了一个“迷你黑洞”。这束强烈的激光从内到外摧毁了分子,只留下一个空洞,类似太空中的黑洞,研究人员希望,这一出乎意料的结果或许将推动病毒和细菌的整体成像技术发展,并帮助科学家开发新型药物。

 

堪萨斯州立大学的研究者是在对一个小分子进行X射线激光测试时制造出这个“分子黑洞”的。单束激光脉冲把分子中最大的原子从里到外差不多“清空”了,只留下几个电子。此时该原子变成了一个空洞,正不断将分子其他部分的电子拖进去,就像黑洞在吞噬周围螺旋形的物质盘。

当用直线加速器相干光源(Linac Coherent Light Source,LCLS)照射分子时,在30飞秒(千万亿分之一秒)内,这个分子失去了超过50个电子,导致其发生爆炸。LCLS常用于生物学个体——包括病毒和细菌——的成像。研究人员希望通过这个分子黑洞的实验结果,可以更好地利用这种激光,进行更多有价值的实验。

“对于任何使用强X射线对样品进行聚焦的实验而言,你都想要了解它如何对X射线做出反应,”参与研究的丹尼尔·罗尔斯(Daniel Rolles)说,“这篇论文表明,我们可以了解小分子的辐射损伤,并对其进行建模。因此,我们现在可以预测在其他系统中会出现什么样的损伤。”

LCLS能够以尽可能高的能量发出X射线,并在样品被激光脉冲摧毁之前记录下数据。论文共同作者Sebasien Boutet说:“它们的强度比你把所有阳光聚焦在地球表面上指甲大小的范围内还要强100倍以上。”

在这项研究中,研究人员用镜子把X射线聚焦到一个直径只有100纳米——比人类头发的宽度还小1000倍——的点上。他们观察了3种类型的样品,包括具有54个电子的单个氙原子,以及两种都具有1个碘原子——拥有53个电子——的分子。

根据此前的研究结果,研究人员预计电子会从原子的外层落进原子内部。这一过程确实发生了,但实验并没有就此停住。碘原子同样会从附近的碳和氢原子中吸收电子,并最终失去总共54个电子。这一扰动和损伤水平不仅超出了研究人员的预料,而且在本质上也具有显著的不同。

“我们认为,这种效应在较大的分子上更为重要,但我们还不知道如何定量测定它,”论文共同作者Artem Rudenko说,“估计有超过60个电子被清除,但我们不知道它在什么地方停下来,因为我们无法探测到分子解体时飞出来的所有碎片,所以也不知道有多少电子消失了。这是我们需要研究的开放性问题之一。”

目前,研究人员希望用LCLS对更复杂的系统进行研究。LCLS的主管迈克·邓恩(Mike Dunne)说:“对于希望获得高解析度生物分子图像的科学家来说,这一研究有很重要的益处,比如,他们可以用这种方法开发出疗效更好的药物。”

https://phys.org/news/2017-06-physicists-extra-superfast-x-ray-probes.html

Physicists squeeze extra data from superfast X-ray probes using machine learning

June 5, 2017 by Hayley Dunning
Physicists squeeze extra data from superfast X-ray probes using machine learning
View of the Linac Coherent Light Source. Credit: SLAC National Accelerator Laboratory

Chemical reactions could be probed in even greater detail using a method invented by Imperial researchers that better characterises ultrafast X-rays.

X-rays can be used to investigate the structures of, and reactions between, molecules on very small scales and at high speed. To do this, scientists use (FELs) to create a train of X-ray pulses.

This allows researchers to probe some of the fundamental processes in chemistry and biology – such as the mechanisms of photosynthesis and the reactions of amino acids, which are the building blocks of life.

However, FELs are inherently unstable, meaning the properties of the resulting X-rays can vary from one pulse to the next. This can lead to inaccuracies in the measurements made using those X-rays.

There are methods to measure the actual properties of produced X-rays, but they can interfere with the experiment, and many will not be able to keep up with the very fast pulse rates of the next generation of X-ray FELs, such as the European XFEL in Hamburg (currently in testing) and the Linac Coherent Light Source II (LCLS-II) in the US.

Now, a research team led by physicists at Imperial College London have used an known as machine learning to accurately predict the properties of X-rays. These predictions are based on certain measurements of the FEL, which can be performed fast enough to match the speed of X-rays.

A thousand times more data

The results of the study, involving 18 research institutions from the UK, Germany, Sweden, the US and Japan, are published today in Nature Communications.

Lead author of the new study Alvaro Sanchez-Gonzalez from the Department of Physics at Imperial said: "For current instruments, which generate about a hundred pulses per second, the slow nature of X-ray characterisation means that sometimes up to a half of the data is unusable.

"This problem will only be compounded in next-generation instruments, such as the European XFEL or LCLS-II, designed to generate hundreds of thousands of pulses per second.

"Our method effectively resolves the problem, and should work on the new instruments as well as the older ones we tested it on. This will allow useful data to be gathered up to a thousand times faster."

The speed of the technique means could be explored in greater detail, as changes in the molecules could be observed on shorter timescales, down to single femtoseconds (one quadrillionth of a second).

Lead researcher Professor Jon Marangos from the Department of Physics at Imperial said: "These rapid-fire experiments will allow us to observe interactions that usually happen too fast for us to capture.

"They will also allow researchers to build up 'molecular movies' of these ultrafast process, for example to see how atoms and even the faster electrons move during a chemical reaction"

Predicting X-ray properties

The researchers knew that there were hundreds of variables in the FEL that could potentially be used to predict the X-ray properties, but it would take a long time to manually check each of these. So the team – which included final year MSci undergraduate students Paul Micaelli and Charles Olivier at Imperial – created a machine learning programme to do the work for them.

Machine learning involves software designed to trawl large datasets for patterns, build models, and then test predictions based on those models, improving as they go along. They used data from the SLAC National Accelerator Laboratory at Stanford University, US, to train some of these models to automatically find key variables and correlations that could be used to predict the X-ray properties with high accuracy.

The team hope their method could be installed directly into X-ray FEL instruments, allowing researchers around the world who access them to benefit from the greater data pool without applying the programme separately themselves.

Explore further: Scientists watch a molecule protect itself from radiation damage

More information: Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning. Nature Communications. dx.doi.org/10.1038/ncomms15461


Comments are closed.



无觅相关文章插件