Static and dynamic light scattering have evolved into powerful methods to investigate a variety of soft and biological matter systems with structures on the nanometer to micrometer scale. They can provide detailed quantitative information on the shape, internal structure, size, and polydispersity of the system as well as interparticle interactions. I will present their fundamentals from a physics and instrumental point of view and also comment on experimental data analysis. The opportunities they offer will be discussed as well as their limits. This will be illustrated by a selection of examples, ranging from colloidal suspensions, detergent and polymer solutions to proteins and include topics like contrast and absolute intensity, determination of molar mass, polydispersity and interparticle interactions.
Photoluminescence Spectroscopy for studying Electron-Hole pair recombination ...RunjhunDutta
Description of Photoluminescence Spectroscopy: Principle, Instrumentation & Application.
Three research papers have been summarized which lay stress on Photoluminescence Study for Electron-Hole Pair Recombination for characterizing the properties of semiconductors used in Photoelectrochemical Splitting of Water.
Dynamic light scattering (DLS) is a technique in physics that can be used to determine the size distribution profile of nanoparticles in suspension or in polymers
Nmr nuclear magnetic resonance spectroscopyJoel Cornelio
Basics of NMR. Suitable for UG and PG courses.
Includes principle, instrumentation, solvents. chemical shift and factors affecting it. Some problems. resolving agents, coupling constant and much more
Photoluminescence Spectroscopy for studying Electron-Hole pair recombination ...RunjhunDutta
Description of Photoluminescence Spectroscopy: Principle, Instrumentation & Application.
Three research papers have been summarized which lay stress on Photoluminescence Study for Electron-Hole Pair Recombination for characterizing the properties of semiconductors used in Photoelectrochemical Splitting of Water.
Dynamic light scattering (DLS) is a technique in physics that can be used to determine the size distribution profile of nanoparticles in suspension or in polymers
Nmr nuclear magnetic resonance spectroscopyJoel Cornelio
Basics of NMR. Suitable for UG and PG courses.
Includes principle, instrumentation, solvents. chemical shift and factors affecting it. Some problems. resolving agents, coupling constant and much more
The distribution and_annihilation_of_dark_matter_around_black_holesSérgio Sacani
Uma nova simulação computacional feita pela NASA mostra que as partículas da matéria escura colidindo na extrema gravidade de um buraco negro pode produzir uma luz de raios-gamma forte e potencialmente observável. Detectando essa emissão forneceria aos astrônomos com uma nova ferramenta para entender tanto os buracos negros como a natureza da matéria escura, uma elusiva substância responsável pela maior parte da massa do universo que nem reflete, absorve ou emite luz.
this presentation includes basics of laser, introductory concepts of light and its development. contribution of light in laser technology and then introduction to laser.
This is a schrodinger equation and also Heiseinberg's uncertainty principle.
It is necessary to know this equation for the quantum mechanic. The wave equation, uncertainty principle of Heisenberg, time dependent and independent of schrodinguer...
An exact solution to Maxwell’s equation for a Sphere applied to Silver Nanopa...AI Publications
With in this work the exact solution to Maxwell’s equation for a sphere, sometimes called Mie theory, is applied to silver nanoparticles embedded in various constant index materials. The albedo, or fraction of the incident light which is scattered away from the particle is calculated and plotted as a function of the size of the particles relative to the incident wavelength.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
6. Physical Origin of Scattered Light
• An electron in the atomic cloud is subject
to a force due to the electric field
• The cloud deforms and a dipole is induced
• As the field oscillates so does the dipole
moment
• The resulting charge movement radiates
(“scatters”) light
• “Elastic” scattering: momentum is
preserved, no energy loss ⇒
+
–
Light
7. A Collection of Atoms
Field scattered by a dipole of momentum
Spherical
Wave
Scattering
Geometry
8. A Collection of Atoms
By definition of polarizability
For an object smaller than λ (Clausius-Mossotti Relation):
9. A Collection of Atoms
Spherical
Wave
Contrast:
Ability to scatter of
the material
Piecing everything together:
Scattering
Geometry
10. Origin of the Scattering Contrast
• Interference
• For a larger object it is
possible to find a second
lump that scatters out of
phase and with the same
amplitude
• Completely destructive
interference
• For an infinite object it is
always possible to do this
⇒ No contrast
11. The Measured Quantity:
the Scattered Intensity
• Whatever the detection technology, the observable quantity
is not the electric field but the flux of energy, the so-called
light intensity
• It can be shown that in most conditions
• In practice the intensity
fluctuates in time
• In SLS experiment the
average intensity is measured
13. RGD Theory
• Assumption: The field inside the particle is the incident
field
• To satisfy this assumption we must require for the
incident field:
i) no reflection at the particle/solvent interface
ii) no phase change within the particle
i) ii)
• Every small lump in the particle scatters as if it were
“alone”
14. RGD Interference: The Scattering Vector
Interference
Term
Dropping the dummy time
dependency terms:
15. The Meaning of the Scattering Vector
The module of the scattering vector has dimensions of
inverse of length:
q-1 is the length-scale of the interference phenomenon.
Two material lumps farther than q-1 interfere
destructively.
Closer than q-1 interfere additively
Destructive
Interference,
Smaller
Intensities
No internal
Interference
Maximum
Intensity
q-1 can be interpreted
as a rough measure of
the probed length-scale
16. One Particle: The Scattering
Amplitude
Integrating previous equation over the whole particle:
Labeling the particle with the subscript j and factoring out
its position by means the variable substitution
We obtain
Particle j scattering
amplitude
Interference :
Particle Position
Internal
Interference
17. An Ensemble of Particles
Previous result allows to sum
each contribution
= 0 = 0 !
Independent position and orientation
We measure a time average:
18. RGD Scattered Intensity
Indeed the electric field in not an observable But
intensity is:
Average
Contrast
Structure Factor:
Interparticle
Interference
Form Factor:
Intraparticle
Interference
Scattering
Geometry
• The RGD assumption results in the factorization of
different contributions
• Same factorization for polydisperse systems
After many manipulations:
19. The Ergodic Hypothesis
One of the starting hypotheses od statistical
mechanics is the so called “Ergodic Hypothesis”:
For any system at equilibrium infinite time
averages of observable quantities are equivalent to
ensemble averages, i.e.:
An ensamble average, is an average over the ensable
of all the feasible physical configurations.
Once we know how to construct such an ensemble this
hypothesis enables us to “calculate” observed time
averaged quantities
21. Form Factor
Non radially symmetric shapes
Scattering length density weighted
pair distance function:
22. Form Factor at Small q: The
Radius of Gyration
Expanding in series the interference factor...
The form factor becomes
Radius of Gyration:
In a plot of the intensity vs. q2 the extrapolation to zero
yields a size parameter that is model independent
25. The Structure Factor
In the same vein as the form factor it can be shown that accounting only
for pair interactions (“on the pair level”)
Applying the Ergodic Hypothesis:
26. The Rayleigh Ratio
Scattered
Intensity
The Rayleigh Ratio: Scattered intensity per unit incident
intensity, unit solid angle, and unit scattering volume.
Depends only on the thermodynamic state of the solvent
not on the measuring apparatus
Mass
Concentration
Sample
Contrast
Molar MassInstrumental
Constant
Solvent
Scattering
(Background)
27. Absolute Measurements
• Knowledge of the constant A enables absolute intensity
measurements
• Absolute measurements allow for the determination of
the radius of gyration and the second virial coefficient
but also of the molar mass M, or the particle
concentration
• How do we do it?
Excess Rayleigh Ratio:
28. Absolute Measurements: How
• Scientists have built special devices that allow the
measurement of Rayleigh ratios, values for common
reference solvents are available in literature
• If we measure the same reference solvent in the same
thermodynamic conditions we have:
Substituting back:
30. Macromolecular Systems
• The treatment so far was focused on particulate systems
• For macromolecular systems we cannot precisely define a
refractive index, how do we obtain the contrast?
Tabulated or
Measured
Assuming a refractive index mixing rule and in dilute conditions:
RGD Hypothesis: m close to 1
33. Particle Dynamics in Real
and Reciprocal Space
Particle tracking with a microscope
Dynamics in reciprocal (Fourier)
space
34. Brownian Motion and Intensity
Fluctuations
Brownian motion
• Particle diffusion due
to thermal motion
• Interference effects on
scattered light
• Stokes-Einstein
equation
RANDOM
FLUCTUATION IN
SCATTERED
INTENSITY
36. Field Correlation Function
Upon normalization:
Identical Independent
Particles
But cannot be measured!
The electric field correlation function is important as it
is directly connected to colloid dynamics models
37. Intensity Correlation Function
Assuming the field be a Gaussian stochastic variable:
As for SLS we can measure the intensity correlation function
Omitting the scattering amplitudes Fj :
38. Coherence Area, Siegert
Relationship
• In practical implementations the field
is not always a Gaussian stochastic
variable
• This happens since we sometimes
image more than one coherence area
(“speckle”)
• The signal to noise ratio is lowered
41. Polydisperse Particles: Cumulant
Analysis
Intensity weighted correlation function
Cumulant Expansion:
Typical uncertainty in cv is ± 0.02, i.e. it is hard to determine cv ≤ 0.2 (20%)
For a polydisperse sample
Intensity weighted diffusion coefficient
45. Polymer Properties: Interaction
and Conformation
At the Theta temperature the chain follows the Gaussian chain model,
the second virial coefficient is close but not equal to zero
Berry G. C., J. Chem. Phys. 44, 4550 (1966)
46. Polymer Properties: Chain
Conformation
Dependency of chain size on
solvent “goodness”
Dependency of chain
size on molar mass (at
Theta solvent conditions)
Outer P. et al., J. Chem. Phys. 18, 830 (1950)
47. Dynamic Properties:
DLS Zimm Plot
• Hydrodynamic Radius depends through a certain
scattering/colloid dynamic model on the scattering angle
• DLS Zimm Plot enables the determination of the zero angle, no
interactions hydrodynamic radius
• This quantity is less model dependent
Bantle S. et al., Macromolecules, 1982, 15 (6)
50. Depolarized DLS: Tobacco Virus
Rotational + Translational
decorrelation rate
Translational
decorrelation rate
• The instantaneous
depolarized intensity
depends on position and
orientation
• At short correlation
times decorrelation is
due to translation, at
larger rimes to
translation and rotation
Wada A. Et al., J. Chem. Phys. 55, 1798 (1971)
51. Depolarized DLS: Tobacco Virus
• Short time decorrelation
rate dependence on q
yields D
• Long time decorrelation
rate dependence on q
yields rotational diffusion
(given the value of D)
• Rotational and
translational diffusion
coefficients yield tobacco
mosaic virus dimensions:
Wada A. Et al., J. Chem. Phys. 55, 1798 (1971)
55. 3D Cross-Correlation
• Two simultaneous scattering experiments with
identical scattering volumes and vectors
• Cross-correlation of the two signals suppresses
multiply-scattered light
Single Scattering:
degenerate single
Fourier component
Multiple Scattering:
different Fourier
components
Laser
Sample in index
matching vat
Photon
Detectors
Cross Correlator
Beam
splitter Lens
Lens
Mirror
y
x
z
add fourier space and natural space mention
add contin and regularization method, integral diskretization, matrix inversion, problem, least square formulation, smoothness
add a slide for spherical planar wave and wavevecror and it’s use to calculate phase shifts
Time dependent terms are dummy as they disappear when we calculate intensities
Key concept: R_g and R_h are weighted differently and depend differently on the chain conformation.. it’s clear that R_g is more sensitive as for R:h the solvent “penetrates” the loose expanded chain. When the chain is globule the difference is less marked as the macromolecule is a sphere : R_h = sqrt(3/5)R_g
interaction effects are lost if you dilute – crystallization, gelling, ageing,
Calculated polydispersity is wrong by 1 order of magnitude and difficult to accurately fit size/index
mention interactions can lead to increase of dynamics at high volume fractions, but these effects are an order of magnitude less. Also mention volume fraction here perhaps.
Only singly scattered light at the pricisely defined geometry yields correlated fluctuations - multiply scattered light does not have a common q vector for each detector. Suppression of 3 orders of magnitude.
For multiple scattering, each detector is in general going to see a different q vector. Only single scattered light has a common q vector for the detector pairs and thus correlated. Fluctuations of different fourier components are statistically independent.
Emphasize ease of implementation, describe physically how this is implemented, including correlating at longer timescales. Undersampling.
Current limitations of switching speed is 1 MHz…1 us minimum lag time for correlation function.
Explain how our statistics get worse with multiple scattering. 3d is not doing magic, its simply extracting information that’s already there.
For borderline samples, we get better data. Enables more turbid samples and improved data. In form factor minimum single scattering intensity is very very small – we’re losing the data in the noise. Less than 5% single scattered light!!
Explain how our statistics get worse with multiple scattering. 3d is not doing magic, its simply extracting information that’s already there.
Most important point to drive home here is that what we measure is motion of scatterers. Given our known inputs, we can determine an effective diffusion coefficient. Given validity of stokes einstein, we can apply this. Tends to be a faster measurement than collecting statics, so you can do some time-dependent stuff more easily.