Diamond/Bath/Manchester/Cardiff Collaboration Paul Raithby Jonathan - - PowerPoint PPT Presentation

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Feb 01, 2023 359 likes •837 views

Diamond/Bath/Manchester/Cardiff Collaboration Paul Raithby Jonathan Skelton Lauren Hatcher Long term collaboration between University of Bath, University of Cardiff, University of Manchester and Diamond Light Source. Use pump-probe methods

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SLIDE 1

Diamond/Bath/Manchester/Cardiff Collaboration

Paul Raithby Jonathan Skelton Lauren Hatcher

Long term collaboration between University of Bath, University of Cardiff, University of Manchester and Diamond Light Source.

  • Use pump-probe methods to investigate photo-activated chemical systems
  • Jonathan has preformed all the computational studies which has been vital for this

investigation

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SLIDE 2

Solid-state Linkage Isomers

Simple, crystal-engineering approach:

  • Use bulky, chelating ancillary fragments
  • Photo-inert fragments dominate crystal packing, generating a “reaction cavity”
  • Facilitate high conversion whilst reducing crystal strain and fatigue

N-bound nitro

10 minutes 400 nm (UV) LEDS 100 K [1] M. R. Warren, S. K. Brayshaw, A. L. Johnson, S. Schiffers, P. R. Raithby, T. L. Easun, M. W. George, J. E. Warren, S. J. Teat, Angew. Chem. Int. Ed. 2009, 48, 5711-5714.

O-bound nitrito

[1]

Δ 300 K

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SLIDE 3

Pseudo-steady-state

[Pd(Bu4dien)(NO2)]BPh4

  • Crystal irradiated in-situ at λ = 400 nm
  • Complete,

100% conversion to metastable nitrito-ONO isomer below 200 K

[1] L. E. Hatcher, J.M. Skelton, M. R. Warren, C. Stubbs, E. L. da Silva, P. R. Raithby CrystEngComm, 2016, 18, 4180-4187

  • Fully reversible, with reverse nitrito  nitro

process induced on warming

  • Very fast photoconversion MS threshold

temp (“MS limit”) ~ 220 K Temp / K NO2 Occupancy ONO Occupancy 100 0.00 1.00 200 0.00 1.00 220 0.71 0.29 240 1.00 0.00 250 1.00 0.00 260 1.00 0.00

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SLIDE 4

Being Predictive

  • Combining Arrhenius and JMAK expressions gives expression for ES t1/2
  • Extrapolation allows prediction of t1/2 (and hence lifetimes)

𝑢1

2

(𝑈) = − 1 𝐵𝑓−𝐹𝑏

𝑆𝑈

ln 1 2

1 𝑜

= − 1 𝐵 ln 1 2 𝑓

𝐹𝑏 𝑆𝑈 1 𝑜

Numerical simulation: predict how isomer ratios evolve under different conditions Input = kinetic parameters from solid-state kinetic studies Outputs include: predicted excitation/decay, pseudo-steady-state profiles; pump-probe TR pulse sequences

[1] L. E. Hatcher, J.M. Skelton, M. R. Warren, C. Stubbs, E. L. da Silva, P. R. Raithby CrystEngComm, 2016, 18, 4180-4187

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SLIDE 5

Time-resolved Results

Δ hv

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SLIDE 6

Automatic processing

Quick analysis to determine the photo-conversion of each time-bin is crucial to guide the next set of experiment

  • Images are sorted into time-bins during data collection
  • Diamonds computer cluster was utilised to auto-processed all time-bin simultaneously

using xia2/DIALS (peak finding, indexing, integration and scaling)

  • A series of structure refinement was then automatically completed and statistical

information output Plot produced 5 minutes after end of collection from the auto- processing:

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SLIDE 7

Dataset 0 Ground State LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 8

Dataset 1 Excitation time 4s LEDs ON

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 9

Dataset 2 Excitation time 13s LEDs ON

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 10

Dataset 3 Excitation time 23s LEDs ON

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 11

Dataset 4 Excitation time 32s LEDs ON

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 12

Dataset 5 Excitation time 42s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 13

Dataset 6 Decay time 4s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

slide-14
SLIDE 14

Dataset 7 Decay time 13s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 15

Dataset 8 Decay time 23s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

slide-16
SLIDE 16

Dataset 9 Decay time 33s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 17

Dataset 10 Decay time 42s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 18

Dataset 11 Decay time 52s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

slide-19
SLIDE 19

Dataset 12 Decay time 61s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 20

Dataset 13 Decay time 71s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

slide-21
SLIDE 21

Dataset 14 Decay time 80s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 22

Dataset 15 Decay time 90s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 23

Dataset 16 Decay time 99s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

slide-24
SLIDE 24

Dataset 17 Decay time 109s LEDs OFF KEY Increase in electron density Decrease in electron density

Molecular Movie

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SLIDE 25

Dataset 0 Ground State LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

slide-26
SLIDE 26

Dataset 1 Excitation time 4s LEDs ON

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 27

Dataset 2 Excitation time 13s LEDs ON

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 28

Dataset 3 Excitation time 23s LEDs ON

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 29

Dataset 4 Excitation time 32s LEDs ON

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 30

Dataset 5 Excitation time 42s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 31

Dataset 6 Decay time 4s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 32

Dataset 7 Decay time 13s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 33

Dataset 8 Decay time 23s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 34

Dataset 9 Decay time 33s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 35

Dataset 10 Decay time 42s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 36

Dataset 11 Decay time 52s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

slide-37
SLIDE 37

Dataset 12 Decay time 61s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 38

Dataset 13 Decay time 71s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

slide-39
SLIDE 39

Dataset 14 Decay time 80s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 40

Dataset 15 Decay time 90s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

slide-41
SLIDE 41

Dataset 16 Decay time 99s LEDs OFF

Molecular Movie

KEY Increase in electron density Decrease in electron density

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SLIDE 42

Dataset 17 Decay time 109s LEDs OFF KEY Increase in electron density Decrease in electron density

Molecular Movie

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SLIDE 43

How fast can we go?

  • Using Pump-MultiProbe techniques, the Dectris Pilatus is

limited by the image readout time with millisecond time- resolve at best.

  • For a single time-delay the Pilatus can be electronically gated at

200 ns. To accumulate enough intensity may take numerous hours and would be unrealistic for multiple snapshots along a reaction pathway.

  • Timepix

detector is a continuous readout detector with 25 ns time-resolution.

  • Rather than images, the detector records time

and position of each photon as well as the laser trigger (or pump source) into the data stream.

  • The time-resolution or data binning can be

selected in processing. Tristen/Timepix Pilatus 300K

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SLIDE 44

Can we go even faster?

PORTO laser Andy Dent and Ann Fitzpatrix

  • The PORTO laser provides a tuneable high-repetition rate pulsed

laser for Diamond beamlines. It is portable and can be installed in a suitably equipped experiments hutch within a few days.

  • A wavelength range of 210 nm to 2600 nm can be achieve using

the OPA.

  • The laser pulse width is 290 fs.
  • The variable repetition rate of the laser can be adjusted from a

single pulse up to 600 KHz, which is greater than the orbit frequency of Diamond. Faster speed required the activation light (pump) to be delivered in a short time period. Pulsed laser are ideally suited for these experiments.

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SLIDE 45

How fast can we go?

0.2 0.4 0.6 0.8 1

  • 3

2 7 12 17 22 27 32

101 reflection intensity time (s)

time-pix detector

  • Experimental condition can be optimized by monitoring

a single reflections (LED power, temperature, crystal size etc) before collecting an entire dataset [Pd(Bu4dien)(NO2)]BPh4

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SLIDE 46

Can we go even faster?

18000 18500 19000 19500 20000 20500 21000 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

Counts (photons) Time (microseconds) Photoexciation for AgCu Complex at 10 kHz (-3 -5 5 Reflection) 2 µs binning

Each point is composed of the accumulated number of counts from 1000 seconds Ag2Cu2L4 (L = 2-diphenylphosphino-3-methylindole ligand) Jarzembska K. N.; et. al., Inorg Chem. 2014, 53(19), 10594–10601.

Time zero Laser excitation (390 nm 140 mW)

In collaboration with Radosław Kamiński

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SLIDE 47

Dave Allen, Sarah Barnett, Lucy Saunders, Adrian Wilcox Andy Dent, Ann Fitzpatrick Giulio Crevatin, Nicola Tartoni, David Omar I19 team Collaborators Paul Raithby, Lauren Hatcher, Jonathon Skelton, Anuradha Pallipurath, Clare Stubbs, Radosław Kamiński

Thank you for listening

Ben Williams, Noemi Frisina, Graeme Winter, Markus Gerstel and Richard Gildea, Paul Hathaway and Andrew Foster and William Nichols Data processing, Controls and Software

4 4

Acknowledgments