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Abstract Graphene oxide (GO) possesses specific properties that are revolutionizing materials science, with applications extending from flexible electronics to advanced nanotechnology. A key method for harnessing GO’s potential is its laser-induced reduction, yet the exact mechanisms — photothermal versus photochemical effects — remain unclear. Herein, we discover the dominant role of photochemical reactions in the laser reduction of GO under visible light, challenging the prevailing assumption that photothermal effects are dominant. Employing a combination of Raman thermometry, X-ray photoelectron and photoluminescence spectroscopies, and electrical atomic force microscopy, we quantify the temperature and map the reduction process across micro and nano scales. Our findings demonstrate that the photochemical cleavage of oxygen-containing groups below a reduction threshold temperature is a decisive factor in GO reduction, leading to distinct characteristics that cannot be replicated by heating alone. This work clarifies the fundamental mechanisms of GO transformation under visible laser irradiation, highlighting the dominant role of photochemical processes. Distinguishing these subtleties enables the development of laser-reduced GO platforms for graphene-based applications compatible with industrial scales. We illustrate this potential by encoding information on GO surfaces as optical storage, allowing us to write binary sequences in long-term memory encoding invisible even through an optical microscope.

A tool that quickly calculates the dominant colors of an image can be very useful in image processing. The k-means clustering algorithm has this potential since it partitions a set of data into n clusters and returns a representative data point from each cluster. We discuss k-means with sampling for images, which applies k-means clustering to a random sample of image pixels. We found that even with a small random sample of pixels from the image, k-means with sampling exhibits no significant loss of correctness. We examine the usefulness and limitations of k-means clustering in determining the prominent colors of an image and identifying trends in large sets of image data.

Herein, it has been shown that betulin can be transformed into its biologically active oxo-derivatives (betulone, betulinic and betulonic aldehydes) by liquid-phase oxidation over supported silver catalysts under mild conditions. In order to identify the main factors determining the catalytic behavior of nanosilver catalysts in betulin oxidation, silver was deposited on various alumina supports (γ-alumina and boehmite) using deposition–precipitation with NaOH and incipient wetness impregnation methods, followed by treatment in H2 or O2. Silver catalysts and the corresponding supports were characterized by X-ray diffraction, nitrogen physisorption, inductively coupled plasma optical emission spectroscopy, photoelectron spectroscopy and transmission electron microscopy. It was found that the support nature, preparation and treatment methods predetermine not only the average Ag nanoparticles size and their distribution, but also the selectivity of betulin oxidation, and thereby, the catalytic behavior of Ag catalysts. In fact, the support nature had the most considerable effect. Betulin conversion, depending on the support, increased in the following order: Ag/boehmite < Ag/boehmite (calcined) < Ag/γ-alumina. However, in the same order, the share of side reactions catalyzed by strong Lewis acid centers of the support also increased. Poisoning of the latter by NaOH during catalysts preparation can reduce side reactions. Additionally, it was revealed that the betulin oxidation catalyzed by nanosilver catalysts is a structure-sensitive reaction.