Time–Temperature Integrating Optical Sensors Based on Gradient Colloidal Crystals (Adv. Mater. 40/2021)

Schöttle, Marius; Tran, Thomas; Feller, Tanja; Retsch, Markus

doi:10.1002/adma.202170318

Cited by 5 publications

(10 citation statements)

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“…Additionally, while similar optical studies of inverse polymeric opals have been conducted by applying, e.g., the Kelvin–Voigt model and WLF theory, [ 20 ] our system is difficult to be studied (semi‐)analytically. [ 21 ] Sintering of particulate systems, in general, is a multi‐step process, [ 39,40 ] and the binary and ternary mixtures increase this intricacy. Besides the polymer and particle composition, the surface chemistry may influence the film formation kinetics.…”

Section: Resultsmentioning

confidence: 99%

“…Without a large amount of validation data, it is impossible to identify the prediction capabilities in the distinct areas shown above. Previous publications about TTIs validated their system with a small number of validation samples, [ 15,18,20,21 ] thus, not covering the whole time–temperature regime. Our large amount of validation data allows us to state individual uncertainties for each pair of predicted temperature and time ( Figure ).…”

Section: Resultsmentioning

confidence: 99%

“…Recently, we showed a related material class: mixed colloidal crystals. [ 21 ] These make use of adjustable dry‐sintering kinetics [ 22,23 ] and show great potential regarding evaluation using simple image analysis.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Enabled Image Analysis of Time‐Temperature Sensing Colloidal Arrays

et al. 2023

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Smart, responsive materials are required in various advanced applications ranging from anti-counterfeiting to autonomous sensing. Colloidal crystals are a versatile material class for optically based sensing applications owing to their photonic stopband. A careful combination of materials synthesis and colloidal mesostructure rendered such systems helpful in responding to stimuli such as gases, humidity, or temperature. Here, an approach is demonstrated to simultaneously and independently measure the time and temperature solely based on the inherent material properties of complex colloidal crystal mixtures. An array of colloidal crystals, each featuring unique film formation kinetics, is fabricated. Combined with machine learning-enabled image analysis, the colloidal crystal arrays can autonomously record isothermal heating events -readout proceeds by acquiring photographs of the applied sensor using a standard smartphone camera. The concept shows how the progressing use of machine learning in materials science has the potential to allow non-classical forms of data acquisition and evaluation. This can provide novel insights into multiparameter systems and simplify applications of novel materials.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Machine Learning Enabled Image Analysis of Time‐Temperature Sensing Colloidal Arrays

et al. 2023

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“…6 Thus, it is essential to develop food sensors that can detect the presence or concentration of specific gases through chemical or biological interactions for food quality monitoring and contaminant detection. 7−9 Generally, a food sensor converts a chemical recognition event into readable signals (e.g., electronic, 10,11 acoustic, 12,13 colorimetric 14,15 ). Compared with other types of sensors that need external devices to further transform signals to readable information, colorimetric sensors are more straightforward and convenient food quality indicators, as they are cheap, require no power, can be read by human eyes, and can be interpreted by unskilled operators such as consumers.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, a food sensor converts a chemical recognition event into readable signals (e.g., electronic, , acoustic, , colorimetric , ). Compared with other types of sensors that need external devices to further transform signals to readable information, colorimetric sensors are more straightforward and convenient food quality indicators, as they are cheap, require no power, can be read by human eyes, and can be interpreted by unskilled operators such as consumers. , Recently, various colorimetric sensors have been well investigated to detect different gases, such as oxygen (O 2 ) , and carbon dioxide (CO 2 ). , For the MAP system, besides the aforementioned gases, sulfur dioxide (SO 2 ) gas is also widely used for postharvest food storage, , which could effectively prohibit the infection of gray mold and reduce the survival of foodborne pathogens in fruit and vegetables.…”

Section: Introductionmentioning

confidence: 99%

A Biomass-Based Colorimetric Sulfur Dioxide Gas Sensor for Smart Packaging

et al. 2023

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Sulfur dioxide (SO2) gas, which can effectively prohibit the growth of pathogenic microorganisms, has been internationally used in commercial food packaging to maintain high-quality food and reduce the incidence of foodborne illnesses. However, the current mainstream methods for SO2 detection are either large and expensive instruments or synthesized chemical-based labels, which are not suitable for large-scale gas detection in food packaging. Recently, we discovered that petunia dye (PD), which is extracted from natural petunia flowers, demonstrates a highly sensitive colorimetric response to SO2 gas with its total color difference (ΔE) modulation reaching up to 74.8 and detection limit down to 1.52 ppm. To apply the extracted petunia dye in smart packaging for real-time gas sensing and food-quality prediction, a flexible and freestanding PD-based SO2 detection label is prepared by incorporating PD in biopolymers and assembling the films through a layer-by-layer approach. The developed label is utilized to predict grapes’ quality and safety by monitoring the embedded SO2 gas concentration. The developed colorimetric SO2 detection label could potentially be used as an intelligent gas sensor for food status prediction in daily life, food storage, and supply chains.

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Communicating Supraparticles to Enable Perceptual, Information‐Providing Matter

Reichstein,

Müssig,

Wintzheimer

et al. 2023

Advanced Materials

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Materials are the fundament of the physical world, whereas information and its exchange are the centerpieces of the digital world. Their fruitful synergy offers countless opportunities for realizing desired digital transformation processes in the physical world of materials. Yet, to date, a perfect connection between these worlds is missing. From the perspective, this can be achieved by overcoming the paradigm of considering materials as passive objects and turning them into perceptual, information‐providing matter. This matter is capable of communicating associated digitally stored information, for example, its origin, fate, and material type as well as its intactness on demand. Herein, the concept of realizing perceptual, information‐providing matter by integrating customizable (sub‐)micrometer‐sized communicating supraparticles (CSPs) is presented. They are assembled from individual nanoparticulate and/or (macro)molecular building blocks with spectrally differentiable signals that are either robust or stimuli‐susceptible. Their combination yields functional signal characteristics that provide an identification signature and one or multiple stimuli‐recorder features. This enables CSPs to communicate associated digital information on the tagged material and its encountered stimuli histories upon signal readout anywhere across its life cycle. Ultimately, CSPs link the materials and digital worlds with numerous use cases thereof, in particular fostering the transition into an age of sustainability.

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Time–Temperature Integrating Optical Sensors Based on Gradient Colloidal Crystals (Adv. Mater. 40/2021)

Cited by 5 publications

References 0 publications

Machine Learning Enabled Image Analysis of Time‐Temperature Sensing Colloidal Arrays

Machine Learning Enabled Image Analysis of Time‐Temperature Sensing Colloidal Arrays

A Biomass-Based Colorimetric Sulfur Dioxide Gas Sensor for Smart Packaging

Communicating Supraparticles to Enable Perceptual, Information‐Providing Matter

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