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Laser Induced Breakdown Spectroscopy – A Tool for Imaging the Chemical Composition of Concrete
(2022)
One of the most common causes of damage is the ingress of harmful ions into the concrete, which can lead to deterioration processes and affect structural performance. Therefore, the increasingly aging infrastructure is regularly inspected to assess durability. Regular chemical analysis can be useful to determine the extent and evolution of ion ingress and to intervene in a timely manner. This could prove more economical than extensive repairs for major damage, particularly for critical infrastructure. In addition to already established elemental analysis techniques in civil engineering such as potentiometric titration or X-ray fluorescence analysis, laser-induced breakdown spectroscopy (LIBS) can provide further important complementary information and benefits. The possibilities of LIBS are demonstrated using the example of a drill core taken from a parking garage.
With 8% of man-made CO2 emissions, cement production is an important driver of the climate crisis. By using alkali-activated binders, part of the energy-intensive clinker production process can be dispensed. However, as numerous raw materials are involved in the manufacturing process here, the complexity of the materials increases by orders of magnitude. Finding a properly balanced binder formulation is like looking for a needle in a haystack. We have shown for the first time that artificial intelligence (AI)-based optimization of alkali-activated binder formulations can significantly accelerate research.
The "Sequential Learning App for Materials Discovery" (SLAMD) aims to accelerate practice transfer. With SLAMD, materials scientists have low-threshold access to AI through interactive and intuitive user interfaces. The value added by AI can be determined directly. For example, the CO2 emissions saved per ton of cement can be determined for each development cycle: the more efficient the AI optimization, the greater the savings.
Our material database already includes more than 120,000 data points of alternative binders and is constantly being expanded with new parameters. We are currently driving the enrichment of the data with a life cycle analysis of the building materials.
Based on a case study we show how intuitive access to AI can drive the adoption of techniques that make a real contribution to the development of resource-efficient and sustainable building materials of the future and make it easy to identify when classical experiments are more efficient.
With 8% of man-made CO2 emissions, cement production is an important driver of the climate crisis. By using alkali-activated binders, part of the energy-intensive clinker production process can be dispensed. However, as numerous raw materials are involved in the manufacturing process here, the complexity of the materials increases by orders of magnitude. Finding a properly balanced binder formulation is like looking for a needle in a haystack. We have shown for the first time that artificial intelligence (AI)-based optimization of alkali-activated binder formulations can significantly accelerate research.
The "Sequential Learning App for Materials Discovery" (SLAMD) aims to accelerate practice transfer. With SLAMD, materials scientists have low-threshold access to AI through interactive and intuitive user interfaces. The value added by AI can be determined directly. For example, the CO2 emissions saved per ton of cement can be determined for each development cycle: the more efficient the AI optimization, the greater the savings.
Our material database already includes more than 120,000 data points of alternative binders and is constantly being expanded with new parameters. We are currently driving the enrichment of the data with a life cycle analysis of the building materials.
Based on a case study we show how intuitive access to AI can drive the adoption of techniques that make a real contribution to the development of resource-efficient and sustainable building materials of the future and make it easy to identify when classical experiments are more efficient.
Moisture and salt loads of concrete can significantly change its microstructure and consequently lead to chemical and mechanical degradation. However, the non-destructive investigation of moisture and salt present in concrete is still difficult. In order to address and compare the sensitivity of different methods concrete samples with different pore systems realized by varying the w/z ratios have been fabricated. The focus of this study was put on the analysis of ultrasonic long and trans waves measured on the surface and in transmission mode. The results show clear dependencies of all applied methods. With the radar and microwave methods predominantly changes in the concrete moisture could be detected, whereas the different pore systems were not observed to alter the signals. In contrast for the ultrasound method also the ongoing hydration as well as the nature of the pore system strongly influenced the signals. As a consequence in a subsequent drying experiment it was also tried to delineate the effects of moisture and hydration. All specimens have been re-saturated under pressure and the drying experiment was repeated using the same multi-sensor approach.
Laser-induced breakdown spectroscopy (LIBS) is a spectroscopic method for the analysis of the chemical composition of sample materials. Generally, the measurement of all elements of the periodic table is possible. In particular, light elements such as H, Li, Be, S, C, O, N and halogens can be measured. Calibration with matrix-matching standards allows the quantification of element concentrations. In combination with scanner systems, the two-dimensional element distribution can be determined. Even rough surfaces can be measured by online adjustment of the laser focus. LIBS can also be used on-site with mobile systems. Hand-held systems are available for point measurements.
Common applications include the investigation of material deterioration due to the ingress of harmful ions and their interaction in porous building materials. Due to the high spatial resolution of LIBS and the consideration of the heterogeneity of concrete, the determination of precise input parameters for simulation and modelling of the remaining lifetime of a structure is possible. In addition to the identification of materials, it is also possible to assess the composition for example of hardened concrete, which involves the cement or aggregate type used. Other important fields of application are the detection of environmentally hazardous elements or the material classification for sorting heterogeneous material waste streams during dismantling. Non-contact NDT for “difficult to assess” structures as an example application through safety glass or in combination with robotics and automation are also possible.
In this work, an overview of LIBS investigations on concrete is given based on exemplary laboratory and on-site applications.
NMR is becoming increasingly popular for the investigation of building materials as it is a non-invasive technology that does not require any sample preparation nor causes damage to the material. Depending on the specific application it can offer insights into properties like porosity and spatial saturation degree as well as pore structure. Moreover it enables the determination of moisture transport properties and the (re-)distribution of internal moisture into different reservoirs or chemical phases upon damage and curing. However, as yet most investigations were carried out using devices originally either designed for geophysical applications or the analysis of rather homogeneous small scale (< 10 mL) samples. This paper describes the capabilities of an NMR tomograph, which has been specifically optimized for the investigation of larger, heterogeneous building material samples (diameters of up to 72 mm, length of up to 700 mm) with a high flexibility due to interchangeable coils allowing for a high SNR and short echo times (50 - 80 m s).
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.