#optical microscopy

Strain-induced birefringence in a polycarbonate bar

Applications Polycarbonate is a clear and relatively tough plastic used to make shatterproof windows, lenses and even helmets. It is also used to make compact discs. Sample preparation A four-point bending rig is used to apply stress. Technique Cross-polarised light microscopy Length bar 14 mm Further information The colours in the image are the result of birefringence and relate to the strain-induced alignment of the polycarbonate molecules. Because the specimen is below the glass transition temperature of PC (145 deg C), the material will relax when the stress is removed and there will be no residual alignment. The colours reveal the pattern of stress concentration around the points of contact with the rig, with a neutral axis along the centre and opposite values of birefringence on either side. Contributor J A Curran Organisation Department of Materials Science and Metallurgy, University of Cambridge

Optical microscopy - Bone tissue implant

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AI optical microscope analyzes 2D materials as precisely as human experts

Haozhe "Harry" Wang's electrical and computer engineering lab at Duke welcomed an unusual new lab member this fall: artificial intelligence. Using publicly available AI foundation models such as OpenAI's ChatGPT and Meta's Segment Anything Model (SAM), Wang's team built ATOMIC (Autonomous Technology for Optical Microscopy & Intelligent Characterization)—an AI microscope platform that can analyze materials as accurately as a trained graduate student in a fraction of the time. "The system we've built doesn't just follow instructions, it understands them," Wang said. "ATOMIC can assess a sample, make decisions on its own and produce results as well as a human expert." Published on October 2 in the journal ACS Nano, the findings point to a new era of autonomous research, where AI systems work alongside humans to design experiments, run instruments and interpret data.

10,000 times faster than traditional methods: Computational framework discovers experimental designs in microscopy

For human researchers, it takes many years of work to discover new super-resolution microscopy techniques. The number of possible optical configurations of a microscope—for example, where to place mirrors or lenses—is enormous. Researchers at the Max Planck Institute for the Science of Light (MPL) have developed an artificial intelligence (AI) framework which autonomously discovers new experimental designs in microscopy. The framework, called XLuminA, performs optimizations 10,000 times faster than well-established methods. The researchers' work is published in Nature Communications. Currently, optical microscopy is most widely used in the biological sciences. The ingenuity and creativity of human researchers have led to the discovery of super-resolution (SR) methods, which overcome the classical diffraction limit of light at about 250 nm and enable one to resolve the organization of the smallest functional units of cellular life.

Cu 60, Zn 40 (wt%), air cooled

Processing Homogenised at 850 °C, air cooled [...] Sample preparation Etched in NH4OH/H2O2 Technique Reflected light microscopy Length bar 400 μm / 80 µm Further information This sample has been air cooled. The fast cooling rate changes the morphology to a 'basket weave' appearance known as a Widmanstätten microstructure. The α phase precipitates out of the single β phase during cooling to give α plates in a β matrix. The plates form in order to minimise strain energy. Contributor Prof T W Clyne Organisation Department of Materials Science and Metallurgy, University of Cambridge

Fe, C 0.8 (wt%) steel, eutectoid transformation, normalised at 900ºC

Processing Normalised 900 ºC [...] Sample preparation Nital Technique Reflected light microscopy Length bar 400 μm / 100 µm Further information This steel is of the eutectoid composition. Once the temperature is lowered below the eutectoid temperature the steel becomes simultaneously supersaturated with both ferrite and cementite. A eutectoid transformation results (g to a + Fe3C). The resultant microstructure, known as pearlite, comprises lamellae of cementite (dark) embedded in ferrite (white). The platelets are parallel to each other and do not follow a specific crystallographic direction. Contributor Dr R F Cochrane Organisation Department of Materials, University of Leeds

Framework models light-matter interactions in nonlinear optical microscopy to determine atomic structure

Materials scientists can learn a lot about a sample material by shooting lasers at it. With nonlinear optical microscopy—a specialized imaging technique that looks for a change in the color of intense laser light—researchers can collect data on how the light interacts with the sample, and through time-consuming and sometimes expensive analyses, characterize the material's structure and other properties. Now, researchers at Pennsylvania State University have developed a computational framework that can interpret the nonlinear optical microscopy images to characterize the material in microscopic detail. The team has published its approach in the journal Optica. "Nonlinear optical microscopy is an important tool that can reveal structural information about different materials," said lead author Albert Suceava, doctoral student in materials science and engineering at Penn State.