Periodic Reporting for period 4 - PICOMETRICS (Picometer metrology for light-element nanostructures: making every electron count)
Reporting period: 2022-11-01 to 2024-04-30
The size, shape and chemical content of nanomaterials determine the properties of these tiny pieces of matter having size ranges from 1 to 100 nm. Even just a few stray atoms on a surface can make a difference, for example when it comes to the material's catalytic properties. Therefore, to understand the properties of nanomaterials, it is important to correctly quantify the position and type of every single atom in a nanomaterial even for light elements, such as, lithium or hydrogen.
Transmission electron microscopy (TEM) can visualise nanoscale details by illuminating a sample with fast electrons. However, many nanomaterials quickly degrade under electron irradiation. To overcome this problem, PICOMETRICS developed tools to usher TEM in a new era of nondestructive characterisation of the 3D atomic structure. Clearly, this is important for the design and understanding of novel nanomaterials.
Transmission electron microscopy (TEM) can visualise nanoscale details by illuminating a sample with fast electrons. However, many nanomaterials quickly degrade under electron irradiation. To overcome this problem, PICOMETRICS developed tools to usher TEM in a new era of nondestructive characterisation of the 3D atomic structure. Clearly, this is important for the design and understanding of novel nanomaterials.
The goal of PICOMETRICS was to characterise the 3D atomic structure of nanomaterials in their native state using TEM. However, in order to avoid damage caused by the illuminating electron beam, the number of electrons used to acquire the data needs to be tuned down significantly hampering a quantitative analysis. To overcome this problem, the team combined novel data-driven methods and experimental strategies to locate and identify all atoms in 3D.
A method has been developed to optimise the electron dose. This is a crucial step since on the one hand we need sufficient electrons to get an image of sufficient quality but on the other hand damage caused to the material will increase with a larger electron dose. This problem can be compared to the use of a medical scanner, where one wants to keep the dose as low as possible to prevent damage. This is all much more critical for children than for adults and also here some nanomaterials are more sensitive to damage caused by the electron beam than others.
For beam-sensitive materials, the optimal electron dose is sufficiently lower than the traditionally used electron dose. As a consequence, the signal and contrast that can be observed in the resulting images is lowered as well. To avoid human bias introduced when visually interpreting such images, we developed a statistical model to resolve fuzzy images down to single atoms - even light ones that only weakly scatter electrons and are therefore hard to spot.
Not only do we want to detect light elements from experimental data, we also want to determine the chemical composition in 3D at the atomic scale from low-dose transmission electron microscopy data. Therefore, a combination of statistical techniques and detailed image simulations was proposed. Furthermore, the PICOMETRICS team has developed a novel statistical model to reveal atomic rearrangements in nanoparticles over time. The atoms in a nanoparticle are hiding behind each other in atomic columns. We only see a top view projection of this stack of atoms in the electron microscopy image. Our goal is to find precisely how many atoms hide in each atomic column of the nanoparticle, thus unravelling the atomic structure. Prior research efforts have enabled us to count the number of atoms with single-atom precision. From one snapshot, it is however impossible to gain insight in the dynamics of the nanoparticle. The new model is therefore specifically designed to analyse a series of images. During the recording of this movie, the atoms could move around in the structure. The model estimates how likely it is for an atomic column to lose or gain one or more atoms from one frame of the movie to the next. Using these probabilities, the model reveals the most likely number of atoms in each column at each time as illustrated in the animated image.
In combination with methods developed to extract the 3D atomic structure, this novel strategy of analysing a time series of images opened up new opportunities since we can follow how the structure of nanoparticles changes under realistic conditions such as a gaseous reaction environment or varying temperatures. Such experiments are not only far more complex in practice, they also lead to new challenges since extra distortions appear when imaging in a gas flow. The PICOMETRICS team therefore used artificial intelligence to correct these distortions showing impressive results. Another breakthrough of PICOMETRICS was the development of two highly dose-efficient real-time imaging methods. Both methods have shown to be very promising for investigating beam-sensitive materials, including metal halide perovskites and metal-organic frameworks.
A method has been developed to optimise the electron dose. This is a crucial step since on the one hand we need sufficient electrons to get an image of sufficient quality but on the other hand damage caused to the material will increase with a larger electron dose. This problem can be compared to the use of a medical scanner, where one wants to keep the dose as low as possible to prevent damage. This is all much more critical for children than for adults and also here some nanomaterials are more sensitive to damage caused by the electron beam than others.
For beam-sensitive materials, the optimal electron dose is sufficiently lower than the traditionally used electron dose. As a consequence, the signal and contrast that can be observed in the resulting images is lowered as well. To avoid human bias introduced when visually interpreting such images, we developed a statistical model to resolve fuzzy images down to single atoms - even light ones that only weakly scatter electrons and are therefore hard to spot.
Not only do we want to detect light elements from experimental data, we also want to determine the chemical composition in 3D at the atomic scale from low-dose transmission electron microscopy data. Therefore, a combination of statistical techniques and detailed image simulations was proposed. Furthermore, the PICOMETRICS team has developed a novel statistical model to reveal atomic rearrangements in nanoparticles over time. The atoms in a nanoparticle are hiding behind each other in atomic columns. We only see a top view projection of this stack of atoms in the electron microscopy image. Our goal is to find precisely how many atoms hide in each atomic column of the nanoparticle, thus unravelling the atomic structure. Prior research efforts have enabled us to count the number of atoms with single-atom precision. From one snapshot, it is however impossible to gain insight in the dynamics of the nanoparticle. The new model is therefore specifically designed to analyse a series of images. During the recording of this movie, the atoms could move around in the structure. The model estimates how likely it is for an atomic column to lose or gain one or more atoms from one frame of the movie to the next. Using these probabilities, the model reveals the most likely number of atoms in each column at each time as illustrated in the animated image.
In combination with methods developed to extract the 3D atomic structure, this novel strategy of analysing a time series of images opened up new opportunities since we can follow how the structure of nanoparticles changes under realistic conditions such as a gaseous reaction environment or varying temperatures. Such experiments are not only far more complex in practice, they also lead to new challenges since extra distortions appear when imaging in a gas flow. The PICOMETRICS team therefore used artificial intelligence to correct these distortions showing impressive results. Another breakthrough of PICOMETRICS was the development of two highly dose-efficient real-time imaging methods. Both methods have shown to be very promising for investigating beam-sensitive materials, including metal halide perovskites and metal-organic frameworks.
The new developments within this programme enable us to characterise nanostructures with increasing complexity using low-dose TEM. Previous results have demonstrated the strengths of advanced quantitative methods when applied to materials that are relatively stable under illumination with high electron doses. However, with the developments within PICOMETRICS, we can nowadays clearly go beyond these limits.
We can now theoretically predict the optimal electron dose in advance of an experimental measurement. This dose results in an unambiguous quantification of nanomaterials in their native state with the highest attainable precision. Although the optimal dose may be low and thus result into fuzzy images for beam-sensitive matter, we can resolve individual atoms at the edge of nanoparticles that are simply invisible in the original image. In addition, we can reveal the hexagonal graphene lattice from even noisier data. In combination with our statistical model to analyse a time series of images, atomic rearrangements in nanoparticles over time can be studied. This enables us to beat the atoms at an advanced game of hide and seek, even though they may cheat and change hiding place by moving to another column in the nanoparticle, or even leave the nanoparticle completely. Thanks to the specific dynamic design of the new statistical model, we can reliably analyse a movie of, for example, a platinum nanoparticle as shown in the animated image. From this result, we could investigate how the nanoparticle loses its facetted morphology and provide a measure for the probability and cross section for surface diffusion. This information can lead to a better understanding of surface related phenomena such as catalysis. When applied to images acquired in a gaseous reaction environment, we could for the first time directly measure 3D variations at the atomic scale for a catalyst nanoparticle.
Moreover, the use of novel detectors in combination with artificial intelligence enabled us to access structural information which gets otherwise lost when using conventional detectors. In this manner, we were able to unscramble the type and ordering of atoms of complex nanostructures and study the dynamics of materials in situ. Clearly this is important for the design of a broad range of nanomaterials.
We can now theoretically predict the optimal electron dose in advance of an experimental measurement. This dose results in an unambiguous quantification of nanomaterials in their native state with the highest attainable precision. Although the optimal dose may be low and thus result into fuzzy images for beam-sensitive matter, we can resolve individual atoms at the edge of nanoparticles that are simply invisible in the original image. In addition, we can reveal the hexagonal graphene lattice from even noisier data. In combination with our statistical model to analyse a time series of images, atomic rearrangements in nanoparticles over time can be studied. This enables us to beat the atoms at an advanced game of hide and seek, even though they may cheat and change hiding place by moving to another column in the nanoparticle, or even leave the nanoparticle completely. Thanks to the specific dynamic design of the new statistical model, we can reliably analyse a movie of, for example, a platinum nanoparticle as shown in the animated image. From this result, we could investigate how the nanoparticle loses its facetted morphology and provide a measure for the probability and cross section for surface diffusion. This information can lead to a better understanding of surface related phenomena such as catalysis. When applied to images acquired in a gaseous reaction environment, we could for the first time directly measure 3D variations at the atomic scale for a catalyst nanoparticle.
Moreover, the use of novel detectors in combination with artificial intelligence enabled us to access structural information which gets otherwise lost when using conventional detectors. In this manner, we were able to unscramble the type and ordering of atoms of complex nanostructures and study the dynamics of materials in situ. Clearly this is important for the design of a broad range of nanomaterials.