Isabel

@DIYflixit @nanographs github.com/nanographs/Ope…

@IAtomictek go electrons go! 🟢

@matlabdogboy ah you would like wiki.fa-fo.de

they should let me get all my friends together and do deposition and lithography and nanoprecipitation and EM and and and

@George200kV oh i fkn love this stuff

this is one of the most impressive things ive ever seen in a microscope

@REALFREEDOMCAT anti burger

@beamshift what if burger is electron??

me trying to explain quantum mechanics > so imagine a burger

@JosephJacks_ @Nature interesting, chrysotile asbestos is also sheets rolled into needles but I don’t know if it’s more like a scroll or concentric tubes. the diffraction pattern has a similar streaking too

The paper that named carbon’s microtubules… Thirty-five years ago this month, a single-author letter appeared in @Nature that has since gathered nearly 100,000 citations — one of the most-cited physical sciences papers ever written. Sumio Iijima’s “Helical microtubules of graphitic carbon” (Nature 354, 7 November 1991) is the paper that opened the carbon nanotube era. Iijima is the discoverer of CNTs, and this is the document of the discovery. He took an arc-discharge rig — the same apparatus that had just made C₆₀ mass production possible — and ran it in argon at lower pressure. On the negative electrode he found needles, 4 to 30 nm wide, up to a micron long. Under the electron microscope, each needle pulled apart into nested concentric tubes, like Russian dolls. Two walls in the thinnest case, fifty in the thickest. The smallest hollow core was 2.2 nm — a ring of about thirty carbon hexagons. The hexagons on each tube were not in straight rows along the axis. They wound around it in a helix. The pitch varied from needle to needle, and between tubes within a single needle. He proved it by reading the electron diffraction patterns — the mirror symmetries can only come from helical hexagons paired with top-bottom coincidence of the cylinder walls. Then he did the move that founded the field: he cut the tube along one side and unrolled it… Once you see the graphene sheet rolled into a cylinder, you understand that the angle of the roll determines everything about the tube’s electronic and mechanical character. Figure 4a is the seed of the (n,m) chiral vector formalism that defined nanotube physics. “Helical microtubules of graphitic carbon” … He didn’t call them tubes or fibers. He called them microtubules — the word borrowed wholesale from cell biology, where it has named tubulin polymers in every living cell since the 1960s. Biological microtubules are hollow tubes built from repeating protein dimers arranged helically around the lumen, 25 nm in outer diameter, with properties dominated by lattice geometry. Carbon nanotubes, as Iijima describes them, are hollow tubes built from repeating carbon hexagons arranged helically around the lumen, of comparable diameter, with properties dominated by lattice geometry. In the early 1990s, Roger Penrose was looking for a biological substrate for objective reduction, and @StuartHameroff was arguing that the brain’s microtubules ran quantum computations along their tubulin lattice. Their first joint Orch-OR paper landed in 1996. Iijima’s landed in 1991. Two communities found the same shape from opposite ends of the periodic table in the same five years and called it by the same name.

@aryamman_bhatia @hackerfabindia pretty

plasma is brutal! @hackerfabindia

@IAtomictek @macona what column pressure gauge reading are you getting to? you might need to clean the gauge itself

@macona Ethanol

High voltage failure occurs, possibly due to electron gun arcing or dielectric breakdown from the high voltage cable.

@IAtomictek interesting, what are the symptoms? did you get a beam and then it arced or are you unable to bring up HT?

@beamshift im new to this, whats a label?

two pics for the timeline to enjoy this evening a - most beautiful ceiling ever seen in a shipping container b - my electrical panel so yall can tell me how to make it better

My new dual-beam SEM - FEI Versa 3D zeptobars.com/en/read/SEM-FE… via @Zeptobars

@TheHatAI what’s your angle here? do you think i built what’s in the photo? are you pro or anti putting your son in a faraday cage?

That's not a Faraday cage, where's the ground for a start and why is it a mosquito net???? You'd have thought science lady would have spotted that But she/her does have pronouns in the bio Probably should have spent more time science'ing

the iPhone magnetometer tops out at 100Hz but you can work around that with a clever sampling/FFT analysis trick: image-ppubs.uspto.gov/dirsearch-publ…

here’s something electron microscopists and crunchy parents can both get behind: the only app that used iPhone magnetometer data to measure 60Hz AC magnetic fields isn’t in the App Store anymore. where did it go? someone should make a new one

you don’t need to do this unless your son is an electron microscope

TIL a liquid metal ion source (LMIS) can be converted into a field emission electron source: rapidly turn off an operating ion source to freeze the Taylor cone into a solid pointy tip and reverse polarity of high voltage. doi.org/10.1116/1.5844…

closed loop & vectorized scan control enables some really interesting stuff!

Autonomous atomic-scale fabrication: ML-guided electron beams sculpting 2D materials Functional properties of 2D materials like MoS2 are exquisitely sensitive to atomic structure. A metallic Mo6S6 nanowire along a hole edge in semiconducting MoS2 yields a 1D-2D heterostructure with electronic behavior nothing like the pristine monolayer. Building this atom by atom means ejecting sulfur atoms at chosen sites, and earlier automated workflows leaned on lucky statistics, with success rates near 64%. Zijie Wu and coauthors build a fully autonomous platform that closes the loop between imaging, ML, and beam control in a STEM. After each HAADF acquisition, three lightweight models decode the image: a U-net ensemble (ELIT) locates atoms, a second U-net segments collapsed MoS-nanowire regions, and a random forest classifies each site as Mo, S, or sulfur-vacancy-line. The classifier learns from 55 hand-labeled neighborhoods, each encoded by distances and intensity to the 5 nearest atoms. No huge simulated datasets, no GAN style transfer, no foundation model. Selected sulfur sites go to an FPGA-controlled scan routine that places the beam in Archimedean spirals with radially symmetric dose. Three strategies are shown: targeted nanowire growth along a pore edge, freestanding MoS-NWs between two seed pores, and directional growth using DBSCAN and line fitting on the largest cluster. The directional case reaches 65% success over 42 runs, with decoding in about 1 second on a GPU. What stands out from an ML-for-science angle is the deliberate small-data philosophy. For atomic labeling, big vision models trained on simulated images often transfer poorly to experiments. Pairing simple models with intuition-based feature engineering is faster to deploy and easier to tune. For applied R&D in semiconductors, quantum devices, and 2D electronics, this reframes what atomic-scale design means. Teams can specify a target defect topology and let the platform iterate toward it without massive labeled datasets, and the framework should transfer to other TMDs, making it relevant for catalysis, energy materials, and quantum sensors. Paper: Wu et al., npj Computational Materials (2026) — CC BY 4.0 | doi.org/10.1038/s41524…

@fishPointer googly eyes would really take it to the next level

400lbs of pure science