Photons have momentum and exhibit wave-particle duality, as demonstrated through experiments like Compton scattering. The Heisenberg uncertainty principle establishes fundamental limits on precisely measuring certain pairs of physical properties of a quantum system, such as position and momentum. This allows light to be used to view structures smaller than the wavelength of visible light by using particles with small wavelengths like electrons in electron microscopes.
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...
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
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...
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
1. Photon Momentum and Uncertainty Principle
Outline
- Photons Have Momentum (Compton Scattering)
- Wavepackets - Review
- Resolution of an Optical Microscope
- Heisenberg’s Uncertainty Principle
2. TRUE / FALSE
1. The photoelectric effect was used to show that light
was composed of packets of energy proportional to
its frequency.
2. The number of photons present in a beam of light is
simply the intensity I divided by the photon energy
hν.
3. Infrared light at a wavelength of 1.24 microns has
photon energy of 1.5 eV.
3. So is Light a
Wave or a Particle ?
Light is always both
Wave and Particle !
On macroscopic scales, large number of photons look
like they exhibit only wave phenomena.
A single photon is still a wave, but your act of trying to
measure it makes it look like a localized particle.
4. Do Photons Have Momentum ?
What is momentum ?
Photons have energy and a finite velocity so there
must be some momentum associated with photons !
Just like Energy,
TOTAL MOMENTUM IS ALWAYS CONSERVED
6. Compton found that if you treat the photons as if they were particles
of zero mass, with energy and momentum .
the collision behaves just as if it were two billiard balls colliding !
(with total momentum always conserved)
In 1924, A. H. Compton performed an experiment
where X-rays impinged on matter,
and he measured the scattered radiation.
It was found that the scattered
X-ray did not have the same
wavelength !
Compton Scattering
incident
photon
target
electron
at rest
recoil
electron
scattered
photon
Image by GFHund http://commons.
wikimedia.org/wiki/File:Compton,
Arthur_1929_Chicago.jpg
Wikimedia Commons.
7. Manifestation of the Photon Momentum
Conservation of linear
momentum implies that an
atom recoils when it
undergoes spontaneous
emission. The direction of
photon emission (and atomic
recoil) is not predictable.
A well-collimated atomic
beam of excited atoms
will spread laterally
because of the recoil
associated with
spontaneous emission.
A source emitting a spherical
wave cannot recoil, because
the spherical symmetry of
the wave prevents it from
carrying any linear
momentum from the source.
SOURCE EMITTING A PHOTON
SOURCE EMITTING AN EM WAVE
excited atom
de -excited atom
photon
source of
excited atoms collimating
diaphragms
beam spreads laterally
because of spontaneous
emission
8. SOLAR SAIL
at rest
INCOMING PHOTONS
1000 W/m2
every second
photons with momentum
+ (1000 J/m2)/c impact the sail
SOLAR SAIL
moves
REFLECTED PHOTONS
1000 W/m2
every second
photons with momentum
- (1000 J/m2)/c leave the sail
with
momentum
+ (2000 J/m2)/c
… and gets that
much more
momentum
every second …
Pressure acting on the sail = (2000 J/m2) /c /second = 6.7 Newtons/km2
Photon Momentum - Moves Solar Sails
Photon Momentum - Moves Solar Sails
Image by D. Kassing http://en.wikipedia.org/wiki/File:SolarSail-
DLR-ESA.jpg on Wikipedia
Image in the Public Domain
9. Optoelectric Tweezers
Transforms optical energy to electrical energy through the
use of a photoconductive surface. The idea is similar to
that used in the ubiquitous office copier machine. In
xerography, a document is scanned and transferred onto a
photosensitive drum, which attracts dyes of carbon
particles that are rolled onto a piece of paper to reproduce
the image.
In this case, the researchers use a photosensitive surface
made of amorphous silicon, a common material used in
solar cells and flat-panel displays. Microscopic polystyrene
particles suspended in a liquid were sandwiched between a
piece of glass and the photoconductive material. Wherever
light would hit the photosensitive material, it would
behave like a conducting electrode, while areas not
exposed to light would behave like a non-conducting
insulator. Once a light source is removed, the
photosensitive material returns to normal.
Depending upon the properties of the particles or cells
being studied, they will either be attracted to or repelled
by the electric field generated by the optoelectronic
tweezer. Either way, the researchers can use that behavior
to scoot particles where they want them to go.
ITO Glass
Projected
Image
Glass Substrate
Nitride
Undoped a-Si:H
n+a-Si:H
ITO
10x
Light
Emitting
Diode
DMD Microdisplay
10. Review of Wavepackets
WHAT WOULD WE GET IF WE SUPERIMPOSED
WAVES OF MANY DIFFERENT FREQUENCIES ?
LET’S SET THE FREQUENCY DISTRIBUTION as GAUSSIAN
11. Reminder: Gaussian Distribution
μ specifies the position of the bell curve’s central peak
σ specifies the half-distance between inflection points
FOURIER TRANSFORM OF A GAUSSIAN IS A GAUSSIAN
50% of data within
13. Gaussian Wavepacket in Space
Gaussian Wavepacket in Space
GAUSSIAN
ENVELOPE
Gaussian Wavepacket in Space
In free space …
… this plot then shows the PROBABILITY OF
WHICH k (or ENERGY) EM WAVES are
MOST LIKELY TO BE IN THE WAVEPACKET
WAVE PACKET
15. Crookes Radiometer
Today’s Culture Moment
The crookes radiometer spins because of
thermal processes, but he initially guessed it
was due to photon momentum…
invented in 1873
by the chemist
Sir William Crookes
Image by Rob Ireton
http://www.flickr.com
/photos/aoisakana/
3308350714/ from Flickr.
16. Heisenberg realized that ...
In the world of very small particles, one cannot measure any
property of a particle without interacting with it in some way
This introduces an unavoidable uncertainty into the result
One can never measure all the
properties exactly
Werner Heisenberg (1901-1976)
Image in the Public Domain
17. Measuring Position and Momentum
of an Electron
Shine light on electron and detect
reflected light using a microscope
Minimum uncertainty in position
is given by the wavelength of the light
So to determine the position
accurately, it is necessary to use
light with a short wavelength
BEFORE
ELECTRON-PHOTON
COLLISION
electron
incident
photon
18. By Planck’s law E = hc/λ, a photon with a
short wavelength has a large energy
Thus, it would impart a large ‘kick’ to the electron
But to determine its momentum accurately,
electron must only be given a small kick
This means using light of long wavelength !
Measuring Position and Momentum
of an Electron
AFTER
ELECTRON-PHOTON
COLLISION
recoiling
electron
scattered
photon
19. Implications
It is impossible to know both the position and momentum
exactly, i.e., Δx=0 and Δp=0
These uncertainties are inherent in the physical world and
have nothing to do with the skill of the observer
Because h is so small, these uncertainties are not observable in
normal everyday situations
20. Example of Baseball
A pitcher throws a 0.1-kg baseball at 40 m/s
So momentum is 0.1 x 40 = 4 kg m/s
Suppose the momentum is measured to an accuracy
of 1 percent , i.e.,
Δp = 0.01 p = 4 x 10-2 kg m/s
21. Example of Baseball (cont’d)
The uncertainty in position is then
No wonder one does not observe the effects of the
uncertainty principle in everyday life!
22. Example of Electron
Same situation, but baseball replaced by an electron
which has mass 9.11 x 10-31 kg traveling at 40 m/s
So momentum = 3.6 x 10-29 kg m/s
and its uncertainty = 3.6 x 10-31 kg m/s
The uncertainty in position is then
23. Classical World
The observer is objective and passive
Physical events happen independently of whether there is an
observer or not
This is known as objective reality
24. Role of an Observer in
Quantum Mechanics
The observer is not objective and passive
The act of observation changes the physical system irrevocably
This is known as subjective reality
25. One might ask:
“If light can behave like a particle,
might particles act like waves”?
YES !
Particles, like photons, also have a wavelength given by:
The wavelength of a particle depends on its momentum,
just like a photon!
The main difference is that matter particles have mass,
and photons don’t!
26. Matter Waves
Compute the wavelength of a 10 [g] bullet moving at 1000 [m/s].
λ = h/mv = 6.6x10-34 [J s] / (0.01 [kg])(1000 [m/s])
= 6.6x10-35 [m]
This is immeasureably small
For ordinary “everyday objects,”
we don’t experience that
MATTER CAN BEHAVE AS A WAVE
27. Gamma
Rays
X Rays
UV Rays
Infrared
Radiation
Microwaves
Radio
Waves
But, what about small particles ?
Compute the wavelength of an electron
(m = 9.1x10-31 [kg]) moving at 1x107 [m/s].
λ = h/mv
= 6.6x10-34 [J s]/(9.1x10-31 [kg])(1x107 [m/s])
= 7.3x10-11 [m].
= 0.073 [nm]
These electrons
have a wavelength in the region
of X-rays
28. Wavelength versus Size
With a visible light microscope, we are limited to being
able to resolve objects which are at least about
0.5*10-6 m = 0.5 μm = 500 nm in size.
This is because visible light, with a wavelength of ~500 nm cannot
resolve objects whose size is smaller than it’s wavelength.
Bacteria, as viewed
using visible light
Bacteria, as viewed
using electrons!
Image is in the public domain Image is in the public domain
29. Electron Microscope
This image was taken with a
Scanning Electron Microscope (SEM).
SEM can resolve features as small as 5 nm.
This is about 100 times better than can be
done with visible light microscopes!
The electron microscope is a device which uses the
wave behavior of electrons to make images
which are otherwise too small for visible light!
IMPORTANT POINT:
High energy particles can be used to reveal the structure of matter !
Image in the Public Domain
30. SEM of various types of pollen
Image in the Public Domain
32. Summary
Light is made up of photons, but in macroscopic situations
it is often fine to treat it as a wave.
Photons carry both energy & momentum.
Matter also exhibits wave properties. For an object of mass m,
and velocity, v, the object has a wavelength, λ = h / mv
One can probe ‘see’ the fine details of matter by using
high energy particles (they have a small wavelength !)
