Tag: scientific goal

SESAME: The road ahead for the next five years

SESAME: The road ahead for the next five years

The Members of SESAME have recently approved a Five-Year Strategic Plan that sets their vision and goals for the Center during the next five years (2024-2028).

SESAME now has five operational beamlines, three that have been hosting users for a few years and two that have come on stream this year. A sixth is under construction. The Center has a 48-room Guest House where it may accommodate users and a solar power plant that covers all the energy needs of the accelerators, beamlines and SESAME building.

It has an ever-increasing number of registered users (currently 1,538) and if one adds the one call for proposals issued in 2016, 2018, 2019, 2021 and 2022, as well as those received for the first call in 2023, this amounts to a total of 572 proposals. Of these, 294 had been approved.

At today’s date, there are 84 articles that have appeared in peer-review publications for experiments carried out at the three beamlines that have been in operation for a few years, and the average scientific impact factor of the journals in which these papers have been published is 5.4, with 25% of them being in journals having an impact factor greater than 7.

In other words, SESAME has now entered a more user-oriented phase of its evolution. This makes the setting up of sample preparation infrastructures to allow users to take full advantage of the beamlines a priority, and essential upgrades to some of the operational beamlines a must, as well as improvements to the accelerators to enhance machine reliability and performance and beam availability and stability of prime importance. It also makes the setting in place of a series of support laboratories to assist users in carrying out their studies and support the research conducted at the beamlines, as well as an upgrade of the optics for the BM02-IR (Infrared) spectromicroscopy beamline, new focusing optics for the BM08-XAFS/XRF (X-ray Absorption Fine Structure/X-ray Fluorescence) spectroscopy beamline, and the establishment of a pool of sample environment systems to meet the users’ demands crucial. For the moment, three support laboratories are envisaged. They are a ChemLab, MatLab and BioLab. It further makes the installation of a UPS (Uninterruptable Power Supply) and diesel generator for SESAME’s security system and emergency lights, and renovation of the fire alarm system mandatory infrastructure upgrades.

All this is envisaged in what is referred to in the Strategic Plan as Category A activities, in other words, activities that are critical for the sustainability of SESAME’s activity that need to be implemented during the period of the Strategic Plan in order to consolidate the existing beamlines and implement the gaps in the Scientific, Technical and Administrative Sectors. Funding to the amount of US$5.908M is required for these activities. This is divided as follows: US$4.350M for the Scientific Sector, US$1.333M for the Technical Sector and US$0.225M for the Administrative Sector.

Read more on the SESAME website

New Director for massive upgrade into Diamond-II

New Director for massive upgrade into Diamond-II

To cement its position as a world-leading research facility, Diamond Light Source recently revealed plans for a large upgrade called Diamond-II and that is set to strengthen the UK’s global scientific leadership. This will be a transformational upgrade that will enable a huge expansion of UK science capabilities as it involves a coordinated programme of development combining state-of-the-art technology in a new machine, five new flagship beamlines and a comprehensive series of upgrades to its Instruments.

To lead this programme, Diamond has appointed Rob Walden, a Chartered Engineer with over 20 years’ experience in delivering business and process improvement programmes in the aerospace manufacturing engineering industry. This was followed by several years as a senior projects advisor in central government where he was involved in, and delivered, nationwide policy projects as well as helping to develop the programme delivery framework for government. Rob was also part of the Cabinet Office’s Gateway Assurance review team and conducted a number of forensic assurance delivery reviews for programmes of national interest. Additionally, he helped to set up the national programme office structure for Highways England and ran two busy Project Management Offices.

Rob joined Diamond Light Source from Sellafield Ltd where he focused on raising the standards of the programme delivery framework, which included the appointment and development of the SRO (Senior Responsible Officer) function for major projects of national interest. Rob comments:

For over 15 years Diamond has been a leading centre for synchrotron science on the world stage, supporting UK business and academia to undertake cutting-edge research in a diverse set of areas and sectors. I am delighted to join a team of such esteemed colleagues as we move into the next chapter in Diamond’s life, the detailed planning of the delivery of Diamond-II to secure long-term funding, pushing the boundaries of scientific research even further and keeping the UK at the forefront of scientific research.

Read more on the Diamond website

Image: Rob Walden, programme director for Diamond-II

Credit: Diamond Light Source

Pushing the limits of science and technology every day

Pushing the limits of science and technology every day

Silvia Forcat is a mechanical engineer working at MAX IV in Sweden. Her role as floor coordinator involves coordinating a wide range of projects for the beamlines. Silvia says, “What inspires me to do my job is to know that I’m contributing to this country’s research and in science in general. There are so many experiments happening in this type of facility and many of them turn into publications. Also my dream would be that one of these publications will get the Nobel Prize. You never know!”

Physics on Autopilot

Physics on Autopilot

Brookhaven National Lab applies AI to make big experiments autonomous

As a young scientist experimenting with neutrons and X-rays, Kevin Yager often heard this mantra: “Don’t waste beamtime.” Maximizing productive use of the potent and popular facilities that generate concentrated particles and radiation frequently required working all night to complete important experiments. Yager, who now leads the Electronic Nanomaterials Group at Brookhaven National Laboratory’s Center for Functional Nanomaterials (CFN), couldn’t help but think “there must be a better way.”

Yager focused on streamlining and automating as much of an experiment as possible and wrote a lot of software to help. Then he had an epiphany. He realized artificial intelligence and machine-learning methods could be applied not only to mechanize simple and boring tasks humans don’t enjoy but also to reimagine experiments.

“Rather than having human scientists micromanaging experimental details,” he remembers thinking, “we could liberate them to actually focus on scientific insight, if only the machine could intelligently handle all the low-level tasks. In such a world, a scientific experiment becomes less about coming up with a sequence of steps, and more about correctly telling the AI what the scientific goal is.”

Yager and colleagues are developing methods that exploit AI and machine learning to automate as much of an experiment as possible. “This includes physically handling samples with robotics, triggering measurements, analyzing data, and – crucially – automating the experimental decision-making,” he explains. “That is, the instrument should decide by itself what sample to measure next, the measurement parameters to set, and so on.”

Read more on the Brookhaven website

Image: Example dataset collected during an autonomous X-ray scattering experiment at Brookhaven National Laboratory (BNL). An artificial intelligence/machine learning decision-making algorithm autonomously selected various points throughout the sample to measure. At each position, an X-ray scattering image (small squares) is collected and automatically analyzed. The algorithm considers the full dataset as it selects subsequent experiments.

Credit: Kevin Yager, BNL