Multimodal Chemical Imaging for Linking Adhesion with Local Chemistry in Agrochemical Multicomponent Polymeric Coatings

Abstract

Seed coatings improve germination and offer higher crop yields through a blend of active ingredients (such as insecticides and fungicides), polymers, waxes, fillers, and pigments. To reach their full potential, fundamental formulation challenges bridging structure and function need to be addressed. In some instances, during industrialvolume packing and transportation, coated seeds do not flow well through elevators, conveyers, and applicators, which may reduce yield and add cost. In this work, we illustrate a combinatorial chemical imaging approach to study seed coatings at the microscale to link chemical and physical properties responsible for low seed flowability. The local chemical composition was examined using time-of-flight secondary ion mass spectrometry (ToF-SIMS) and at comparable length scales, the local adhesive properties were examined using atomic force microscopy (AFM) force volume mapping. The link between the chemical and the adhesive properties was established by non-negative matrix factorization (NMF). The correlative multimodal imaging approach developed here utilizing AFM force volume mapping, ToF-SIMS chemical mapping, and data analytics offers a path for linking function with localized chemistry when investigating multicomponent soft material systems.

Controlling orientation, conformation, and biorecognition of proteins on silane monolayers, conjugate polymers, and thermo-responsive polymer brushes: investigations using TOF-SIMS and principal component analysis

“…PCA could also be applied to an analysis of TOF-SIMS 2D images [33,[58][59][60] and even 3D TOF-SIMS sputtering data [50]. Other multivariate methods already applied for TOF-SIMS data analysis are non-negative matrix factorization (NMF) [61,62], the k-means cluster method [63], discriminant analysis [64,65], and artificial neuronal networks [55,66] involving self-organizing maps [67][68][69][70][71].…”

Development of Machine-Learning Techniques for Time-of-Flight Secondary Ion Mass Spectrometry Spectral Analysis: Application for the Identification of Silane Coupling Agents in Multicomponent Films

“…Recently, machine learning was applied to analyze ToF-SIMS spectra to improve their interpretability. In particular, unsupervised learning methods such as principal component analysis, − multivariate curve resolution, − non-negative matrix factorization, , the self-organizing map, , and the autencoder , have been useful in detecting the distributions of specific components in a mixture sample. However, the identification of components in a uniform mixture film without distribution remains an issue.…”

Evaluation of Time-of-Flight Secondary Ion Mass Spectrometry Spectra of Peptides by Random Forest with Amino Acid Labels: Results from a Versailles Project on Advanced Materials and Standards Interlaboratory Study

“…Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is one of the most promising chemical imaging methods and powerful surface analysis techniques in terms of high spatial resolution of approximately 100 nm, extremely high surface sensitivity, and analysis depth of less than 2 nm. − On the other hand, the interpretation of TOF-SIMS spectra is difficult because a peak is generally related to several different molecules and a molecule produces a variety of peaks, which are mostly difficult to predict due to complicated fragmentation mechanisms. Data analysis methods such as multivariate analysis have been applied to TOF-SIMS data to manage this issue. − Multivariate analysis techniques such as principal component analysis (PCA) and non-negative matrix factorization (NMF) are powerful tools for the interpretation of TOF-SIMS data. − PCA is one of the best unsupervised learning methods to understand the outline of an unknown TOF-SIMS data. Moreover, new methods including machine learning and deep leaning techniques were also applied to TOF-SIMS data to classify highly similar materials. − However, the identification of mass peaks in TOF-SIMS spectra to identify completely unknown samples remains one of the most important issues.…”