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Hydrogen peroxide (H2O2), a strong oxidizer, is frequently used for chemical sterilization in aseptic food processing. Due to their inherent robustness, bacterial spores, i.e. spores of Bacillus atrophaeus, are commonly employed to evaluate the efficacy of the sterilization procedure [1]. Despite its widespread use, however, the details of the sporicidal action of H2O2 are not yet fully understood, with various mechanisms being discussed in the literature [2,3]. Here, we employ optical trapping Raman microscopy to elucidate the sporicidal mechanism of H2O2. Optical trapping Raman microscopy allows us to isolate, investigate, and manipulate individual spores from a sample (Fig. 1A). Combining optical microscopy with the real-time, label-free chemical information obtained from Raman spectroscopy directly reveals the sequence of steps that lead to oxidative spore death after H2O2 exposure (Fig. 1B). We find that spore degradation begins with the fast, cooperative release of dipicolinic acid (DPA), a key spore biomarker and a major component of spore’s core. This indicates a breach of the inner membrane surrounding the core. The onset of DPA release depends on the concentration of H2O2. DPA outflux is accompanied by the formation of oxidation products (mostly carbonyl compounds) and oxidation continues after exchange of DPA with the surrounding medium is complete. The remaining spore shell then slowly disintegrates and continues to shrink on a timescale of hundreds of seconds. Simultaneous observation of spore morphology and size by optical microscopy and particle tracking corroborates this three-step mechanism. The presented assay allows on-line monitoring of spore composition and morphology, which directly reveals the mechanism underlying the sporicidal action of H2O2. In addition, trapped spores can be exposed to different chemical or physical stimuli (temperature, light, chemical composition of the surrounding medium, etc.) to investigate a variety of industrially relevant sterilization conditions. Jildeh, Z. B., Wagner, P. H. & Schöning, M. J. Sterilization of Objects, Products, and Packaging Surfaces and Their Characterization in Different Fields of Industry: The Status in 2020. physica status solidi (a) 218, 2000732 (2021). Setlow, P. & Christie, G. What’s new and notable in bacterial spore killing. World J Microbiol Biotechnol 37, 144 (2021). Setlow, P. Spores of Bacillus subtilis: their resistance to and killing by radiation, heat and chemicals. J Appl Microbiol 101, 514-525 (2006). Figure 1

Surface-enhanced Raman spectroscopy (SERS) combines the unique advantages of Raman spectroscopy – namely detection of specific molecular fingerprints without requiring prior labelling – with enhanced sensitivity. SERS takes advantage of the amplification of the incident electric field by localized surface plasmons on noble metal nanoparticle surfaces, which allows the rapid, non-destructive detection of a wide variety of analytes ranging from small molecules to entire microorganisms. The data obtained from SERS experiments, however, can be difficult to interpret. Spectra are dominated by the chemical species in closest contact with the plasmonic field, and consequently spectra recorded at different points in time from the same sample can exhibit considerable differences. The rich chemical information contained in the spectral data therefore is often not utilized to its full potential or lost altogether. Here, we combine deep convolutional neural networks (CNNs) with SERS to classify series of spectra obtained from mixtures of different sample species (1). We find that CNNs outperform standard chemometric methods, such as principal component analysis, but also support-vector machine classifiers, shallow artificial neural networks, or even trained human experts while requiring minimal data preprocessing, such as background subtraction or smoothing. We discuss optimizing the classifier with regards to network topology, learning strategy, and data augmentation. In addition, we show that we can extract useful information from trained networks. In simple cases, activation maximization spectra, which represent the spectral features that are most relevant to discriminate between different sample classes, can provide a good approximation of the spectra in the original sample. We also train generative adversarial networks (GANs) (2) to generate / extract characteristic spectra from large, more heterogeneous datasets. These spectra can provide additional insight into the composition of the sample and might thus be relevant to guide further experiments. Deep learning and SERS are two powerful techniques that complement each other. The high sensitivity of SERS quickly generates large, but often highly complex spectral datasets, which are in turn well-suited to the analysis by CNNs. Most importantly, deep learning not only excels at classification but can be used as a more general tool to facilitate the interpretation of heterogeneous, seemingly intractable datasets. Given the tremendous attention deep learning has attracted over the past decade, we anticipate that it will play an increasingly prominent role in spectral analysis as well. Yang, J. et al., 2019. Deep learning for vibrational spectral analysis: Recent progress and a practical guide. Analytica chimica acta, 1081, pp.6–17. Odena, A., Olah, C. & Shlens, J., 2016. Conditional Image Synthesis With Auxiliary Classifier GANs. arXiv:1610.09585 [stat.ML] Figure 1

In recent years, Raman spectroscopy has evolved into a broadly used analytical method in chemistry, life sciences, and medicine. Here, we combine Raman spectroscopy with optical trapping and optical microscopy to isolate and investigate individual micron-size biological particles from a sample. In this setup, Raman spectroscopy provides a label-free chemical fingerprint while light microscopy reveals the sample morphology. In addition, elastic light scattering as well as observation of diffusive movement in the optical trap’s potential offer information on particle size and refractive index. Single particles can be captured and manipulated for tens of minutes. During this time, the stably trapped particles can be exposed to a variety of chemical and physical stimuli such as heat, light of different wavelengths, or changes in the chemical composition of the particle environment through microfluidics (Fig. 1A). In this work, we employ this optical trapping Raman spectroscopy sensor / actuator / treatment system to study the effect of hydrogen peroxide (H2O2) on individual spores of Bacillus atrophaeus, an important model system to assess the efficacy of industrial sterilization processes. Interestingly, we find B. atrophaeus spores to be stable for more than 30 minutes while optically trapped even at elevated temperatures (> 70 °C). Exposure to H2O2 results in spore degradation in a concentration-dependent manner within several minutes and is accelerated by high temperatures. Raman spectroscopy indicates that spore degradation is characterized by the rupture of the spore core resulting in the fast release of dipicolinic acid, a major spore biomarker that is considered key for spore resistance to environmental stress (Fig. 1B). Oxidative degradation of the spore shell starts before core rupture and continues after dipicolinic acid release. Simultaneous observation of spore morphology corroborates this two-phase model. Illumination with shorter (< 550 nm) wavelength light exposes the spores to oxidative stress via the generation of reactive oxygen species in the spore environment by light, thereby mimicking the H2O2 sterilization process. In the presence of small amounts (0.5%) H2O2, spore lifetime is reduced to several seconds. With the presented system, we address spore activity down to the single cellular level in a multimodal manner: trapping and manipulation of single spores over several tens of minutes (actuator functionality), stimulation of single spores by temperature, illumination, and H2O2 exposure (treatment functionality) as well as on-line monitoring of spore activity (sensor functionality). Figure 1

Electrodeposition process has been widely applied to various areas to form functional thin films and nanostructures. In order to understand the deposition process from molecular level to achieve further precise control, we employ theoretical calculation and surface enhanced Raman scattering (SERS). For the theoretical calculation to study the deposition reaction mechanism, molecular orbital and density functional theory calculations were applied to investigate the behaver of the species at the electrode surface, and various factors such as solvation effect and catalytic activity of the surface were also analysed. In addition, SERS equipped with confocal microscopy was used for in situ analysis of the reaction processes at the electrode surface. For this we applied plasmon sensors [1] to achieve very high sensitivity analysis. Furthermore multi-confocal type SERS microscopy was employed for mapping and imaging analysis. By using these techniques, various systems have been analysed and modelling of their reaction processes has been carried out. This work was financially supported in part by “Development of Systems and Technology for Advanced Measurement and Analysis,” Japan Science and Technology Agency, and Grant-in-Aid for Scientific Research, MEXT, Japan. [1] M. Yanagisawa, M. Saito, M. Kunimoto, T. Homma, Appl. Phys. Express, 9 , 122002 (2016).

Electrochemical deposition processes have been widely applied to form functional thin films and micro/nano structures featuring their controllability and capability to fabricate uniform deposits to wide, non-flat surfaces. For achieving further precise control, we have attempted molecular-level analysis of the deposition processes and reaction mechanisms through theoretical and experimental approaches. In this paper, we will introduce these approaches with some of our resent results. For the theoretical analysis to quantitatively understand the reaction mechanisms, we have applied density functional theory (DFT) calculations focusing on the surface species such as additives, reaction intermediates, reducing agents (for electroless deposition), and so on[1]. Effects of solvation and catalytic activity of the surfaces, etc., were also investigated and behaviours of these species, which are unique to the deposition at solid/liquid interfaces, were elucidated. While these theoretical analysis could predict rigorous mechanisms of the deposition reaction processes, acquiring such molecular-level data through experimental approach is quite challenging since the amount of these surface species are trace and they are “buried” under the electrolyte. For this, we focused on surface enhanced Raman scattering (SERS) equipped with confocal microscopy and developed plasmonic sensors with patterned nanostructures to achieve very high enhancement effect, and we applied them to analyze the electrochemical deposition processes such as the behaviour of additives. For the plasmonic sensors, we have developed “reflection” type and “transmission” type; the “reflection” type sensors consist of metals active for plasmonic enhancement such as Ag, Au, and Cu with centrifuge-grooved nanostructures or nanodot arrays for maximizing the enhancement, which can be directly used as the substrates for the deposition[2]. The “transmission” type sensors, which are single or array of the microlens coated with discrete nanoparticles of the plasmon-active metals, can be applied to various processes regardless of the substrate materials[3]. By using these sensors, we analyzed behaviours of the additives and reductants at the deposition sites. In addition, we have also developed a system for in situ analysis of the processes inside the microscopic pores such as through silicon via (TSV) [4]. A model-pore was fabricated using the sensor (to be a part of “side-wall” of the pore) with the metal nanodot array covered with the structure made of poly(dimethylsiloxane) (PDMS). It was confirmed that Raman signal of the additives could be detected in situ in high sensitivity without interference of the signals originated from PDMS, and their behaviour was investigated in detail. Furthermore, attempts on measuring local temperature as well as pH in the vicinity of the electrode were made by using the SERS measurements, which will be able to provide new insights for precise analysis of the behaviours of surface species during the deposition processes. This work was financially supported in part by “Development of Systems and Technology for Advanced Measurement and Analysis,” Japan Science and Technology Agency, and Grant-in-Aid for Scientific Research, MEXT, Japan. [1] For example, T. Homma, Electrochemistry, 83, 680 (2015). [2] B. Jiang, M. Kunimoto, M. Yanagisawa, T. Homma, J. Electrochem. Soc., 160, D366 (2013). [3] M. Yanagisawa, M. Saito, M. Kunimoto, T. Homma, Appl. Phys. Express, 9 , 122002 (2016). [4] T. Homma, A. Kato, M. Kunimoto, M. Yanagisawa, Electrochem. Commn., submitted.