Versatile Analytical Tool Research Articles

Long-range enhancement for fluorescence and Raman spectroscopy using Ag nanoislands protected with column-structured silica overlayer

We demonstrate long-range enhancement of fluorescence and Raman scattering using a dense random array of Ag nanoislands (AgNIs) coated with column-structured silica (CSS) overlayer of over 100 nm thickness, namely, remote plasmonic-like enhancement (RPE). The CSS layer provides physical and chemical protection, reducing the impact between analyte molecules and metal nanostructures. RPE plates are fabricated with high productivity using sputtering and chemical immersion in gold(I)/halide solution. The RPE plate significantly enhances Raman scattering and fluorescence, even without proximity between analyte molecules and metal nanostructures. The maximum enhancement factors are 107-fold for Raman scattering and 102-fold for fluorescence. RPE is successfully applied to enhance fluorescence biosensing of intracellular signalling dynamics in HeLa cells and Raman histological imaging of oesophagus tissues. Our findings present an interesting deviation from the conventional near-field enhancement theory, as they cannot be readily explained within its framework. However, based on the phenomenological aspects we have demonstrated, the observed enhancement is likely associated with the remote resonant coupling between the localised surface plasmon of AgNIs and the molecular transition dipole of the analyte, facilitated through the CSS structure. Although further investigation is warranted to fully understand the underlying mechanisms, the RPE plate offers practical advantages, such as high productivity and biocompatibility, making it a valuable tool for biosensing and biomolecular analysis in chemistry, biology, and medicine. We anticipate that RPE will advance as a versatile analytical tool for enhanced biosensing using Raman and fluorescence analysis in various biological contexts.

A Universal Plug-and-Play Approach to In Situ Multinuclear Magnetic Resonance Analysis of Electrochemical Phenomena in Commercial Battery Cells.

Advancing electrochemical energy storage devices relies on versatile analytical tools capable of revealing the molecular mechanisms behind the function and degradation of battery materials in situ. The nuclear magnetic resonance phenomenon plays a pivotal role in fundamental studies of energy materials and devices because of its exceptional sensitivity to local environments and the dynamics of many electrochemically relevant elements. The jelly roll architecture is one of the most energy-dense and, therefore, most popular concepts implemented in pouch, prismatic, and cylindrical Li- and Na-ion cells. Such widely commercialized designs, however, represent a significant obstacle for a range of powerful in situ magnetic resonance-based methodologies due to negligible radio frequency electromagnetic field penetration through conductive metal casings and current collectors. In this work, we introduce an experimental setup that enables direct RF wave transmission through the cell terminals and current collectors, and provides efficient excitation and detection of NMR signals. An RF adapter designed as a plug-and-play device effectively turns the battery cell into an NMR probe that can be tuned to a broad range of Larmor frequencies. Due to its exceptional sensitivity, versatility, and multinuclear capability, the proposed methodology is suitable for fundamental research and industrial high-throughput screening applications. In situ NMR lineshapes provide a direct quantitative description of the chemical environments of electrochemically active elements and enable new metrics for the accurate assessment of state-of-charge (SoC) and state-of-health (SoH). Specifically, in commercial pouch cells based on lithium cobalt oxide, lithium nickel manganese cobalt oxide, and sodium nickel iron manganese oxide chemistries, 7Li and 23Na NMR data unambiguously show electrochemical transformations between intercalated and metallic forms of charge carrier ions, and reveal anisotropic magnetic properties of the electrode coating.

Engineering Single Component Luminogens to Multicomponent Charge‐transfer Co‐crystal Substrate as New Frontiers for Sensitive SERS Detection

AbstractOrganic charge‐transfer (CT) co‐crystals have demonstrated remarkable physical properties and have found applications in numerous fields. Yet their utility as a Surface‐Enhanced Raman Spectroscopy (SERS) substrate, a powerful and versatile analytical tool, has never been explored. Herein, three twisted molecular donors are synthesized, that exhibit well‐controlled switchable optical properties including aggregation‐induced emission (AIE), mechanochromic luminescence (MCL), and color‐specific polymorphism. Rapid production of charge‐transfer co‐crystals is also established with a π‐acceptor TCNQ and utilized conceptually as a SERS substrate for methylene blue (MB) detection, exhibiting a very high enhancement factor of 109 and limit of detection of 10−13 m, respectively, due to the presence of low‐lying excited state, exhibit an 80% CT character, originating from the HOMO of the co‐crystal and interacting with the LUMO of the MB molecule. This approach using CT co‐crystals as a SERS substrate presents newer frontiers that require minuscule levels of rapid detection and impact allied areas, helping us understand and optimize the fascinating properties of such multicomponent materials for newer technologies.

Simultaneous SERS-decoding detection of multiple pathogens in drinking water with home-made portable double-layer filtration and concentration device.

Theengineering of a home-made portable double-layer filtration and concentration device with the common syringe for rapid analysis of water samplesis reported. The core elements of the device were two installed filtration membranes with different pore sizes for respective functions. The upper filtration membrane was used for preliminary intercepting large interfering impurities (interception membrane), while the lower filtration membrane was used for collecting multiple target pathogens (enrichment membrane) for determination. This combination can make the contaminated environmental water, exemplified by surface water, filtrated quickly through the device and just retained the target bacteria of Escherichia coli O157:H7, Staphylococcus aureus, and Listeria monocytogenes on the lower enrichment membrane. Integrating with surface-enhanced Raman spectra (SERS) platform to decode the SERS-Tags (SERS-TagCVa, SERS-TagR6G, and SERS-TagMB) already labeled on each of the enriched bacteria based the antibody-mediated immuno-recognition effect, fast separation, concentration, and detection of multiple pathogenic bacteria from the bulk of contaminated environmental water were realized. Results show that within 30min, all target bacteria in the lake water can be simultaneously and accurately measured in the range from 101 to 106 CFU mL-1 with detection limit of 10.0 CFU mL-1 without any pre-culture procedures. This work highlights the simplicity, rapidness, cheapness, selectivity, and the robustness of the constructed method for simultaneous detecting multiple pathogens in aqueous samples. This protocol opens a new avenue for facilitating the development of versatile analytical tools for drinking water and food safety monitoring in underdeveloped or developing countries.

A Review of Mass Spectrometry as an Important Analytical Tool for Structural Elucidation of Biological Products

Mass spectrometry (MS) is one of the most versatile analytical measurement tools available to scientists today and finds application in virtually most aspects of chemistry research. MS is an efficient method to elucidate the structure and chemical composition of different samples or molecule. Recently, it has been used to classify biological products, in particular proteins and protein complexes, in a number of species. The success of mass spectrometry is driven both by innovative instrumentation designs, especially those operating on the time-of-flight or ion-trapping principles, and by large-scale biochemical strategies, which use mass spectrometry to detect the isolated proteins. Due to MS capability to distinguish between substances, it is used to identify unknown substances and its isotopes. When compared with other analytical tools or orthogonal methods used in analytical laboratories, mass spectrometers are often used as it has less background interference since it is performed in a vacuum. The increase in sensitivity and resolution of the MS has opened new dimensions for structural elucidation of biological products. In this review, the recent research application of MS techniques for the structural elucidation of some biological products was presented.

Role of pretty nanoflowers as novel versatile analytical tools for sensing in biomedical and bioanalytical applications

AbstractIn recent years, an encouraging breakthrough in the synthesis of immobilized enzymes in flower‐shaped called “organic‐inorganic hybrid nanoflowers (hNFs)” with greatly enhanced catalytic activity and stability were reported. Although, these hNFs were discovered by accident, the enzymes exhibited highly enhanced catalytic activities and stabilities in the hNFs compared with the free and conventionally immobilized enzymes. Herein, we rationally utilized the catalytic activity of the hNFs for analytical applications. In this comprehensive review, we covered the design and use of the hNFs as novel versatile sensors for electrochemical, colorimetric/optical and immunosensors‐based detection strategies in analytical perspective.

Pendekatan Machine Learning untuk Analisis dan Visualisasi Data Jembatan Timbang

In this research, a machine learning approach, especially a decision tree model, is implemented to improve the analysis and visualization of weighbridge data in Indonesia. The evaluation results show that the decision tree model provides better insight in predicting the carrying capacity, dimensions and loading procedures of vehicles. The advantage of this model lies in its combination of low Mean Squared Error (MSE) and high R-squared, indicating its effectiveness in capturing data variance and providing accurate predictions. The use of decision tree models can be a valuable tool in improving the visualization of bridge weighing data, allowing users to gain additional insights and understand the complex dynamics within the data. In addition, the model's adaptability to various types of data makes it a versatile analysis tool. The positive implications of using this model open up opportunities to understand more deeply the logic of predictions and make more informed decisions. As a suggestion, increasing the number and quality of weighing equipment, wider application of information and communication technology, human resource training, and cross-sector collaboration can further strengthen weighbridge management in Indonesia.

Visualization and quantification of microphase separation in thermoplastic polyurethanes under different hard segment contents and its effect on the mechanical properties

The microstructure and macroscopic properties of thermoplastic polyurethanes (TPUs) are significantly affected by their microphase separation. It is therefore of great interest to quantitatively characterize the microphase separation of TPUs and explore its connection with their mechanical properties. In this work, a method based on atomic force microscopy is proposed to visualize and quantify the microphase separation of polyether TPUs with different hard segment contents. The lateral correlation length was proposed to describe the scale of microphase separation and the microscopic Young's modulus distributions were also obtained for comparison. Results show that the macroscopic Young's modulus of the polyether TPU is consistent with the microscopic Young's modulus only when the lateral correlation length reaches a certain threshold. Besides, the studied polyether TPU exhibits the best overall mechanical properties when it reaches the maximal lateral correlation length. The proposed AFM-based method demonstrates a versatile analytical tool for quantitatively assessing the microphase separation and unveiling the relationship between the microphase separation, microscopic mechanical properties, and the macroscopic properties of TPUs.

A fast, scalable and versatile tool for analysis of single-cell omics data.

Single-cell omics technologies have revolutionized the study of gene regulation in complex tissues. A major computational challenge in analyzing these datasets is to project the large-scale and high-dimensional data into low-dimensional space while retaining the relative relationships between cells. This low dimension embedding is necessary to decompose cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Traditional dimensionality reduction techniques, however, face challenges in computational efficiency and in comprehensively addressing cellular diversity across varied molecular modalities. Here we introduce a nonlinear dimensionality reduction algorithm, embodied in the Python package SnapATAC2, which not only achieves a more precise capture of single-cell omics data heterogeneities but also ensures efficient runtime and memory usage, scaling linearly with the number of cells. Our algorithm demonstrates exceptional performance, scalability and versatility across diverse single-cell omics datasets, including single-cell assay for transposase-accessible chromatin using sequencing, single-cell RNA sequencing, single-cell Hi-C and single-cell multi-omics datasets, underscoring its utility in advancing single-cell analysis.

Using Mass Spectrometry Imaging to Visualize Pesticide Accumulation and Time-Dependent Distribution in Fungicide-Coated Seeds.

Pesticide seed treatment provides efficient crop protection in the early season and enables a reduction in the quantity of fungicides used later. Hence, it has been a practical application for crop protection in major crop sectors such as corn, soybean, wheat, and cotton. The chemicals on pesticide-treated seeds may show different distributions depending on the structure of the seeds and the physical properties of the chemicals, but they have not been well studied because of a lack of versatile analytical tools. Here, we used mass spectrometry imaging to visualize the distribution of a fungicide (ethaboxam) in corn and soybean seeds coated with it. Contrasting distribution patterns were noted, which are likely dependent on the seed structure. We also obtained information on fungicide distribution after the seedings, which will contribute to a better understanding of the fungicide delivery pathway within plants. Using this new analytical method, we were able to obtain hitherto unavailable time-dependent, dynamic information on the ethaboxam. We expect that this method will be a useful tool with widespread applications in pesticide development and use. Copyright © 2023 Shuichi Shimma, Hiromi Saito, Takuya Inoue, and Fukumatsu Iwahashi. This is an open-access article distributed under the terms of Creative Commons Attribution Non-Commercial 4.0 International License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Fluctuation cepstral scanning transmission electron microscopy of mixed-phase amorphous materials

Four-dimensional scanning transmission electron microscopy (4D-STEM) is a versatile analytical tool for characterizing materials structural properties. However, extending such analysis to disordered materials is challenging, especially in technologically important samples with mixed ordered and disordered phases. Here, we present a new 4D-STEM method, called fluctuation cepstral STEM (FC-STEM), based on the fluctuation analysis of cepstral transform of diffraction patterns. The peaks in the associated transformation relate to inter-atomic distances in a thin sample. By varying the real-space range over which fluctuations are calculated, distinct ordered and disordered phases can be mapped in a diffractive image reconstruction. We demonstrate the principles of FC-STEM by characterizing a silicon anode, harvested from a cycled lithium-ion battery. A mixture of amorphous and nanocrystalline silicon, graphitic carbon, and electrolyte by-products is identified and mapped. Comparisons with conventional electron imaging and energy-dispersive X-ray spectroscopy show that FC-STEM is highly effective for the structure determination of mixed-phase amorphous materials.

Impact of Regional Mobility on Air Quality during COVID-19 Lockdown in Mississippi, USA Using Machine Learning.

Social distancing measures and shelter-in-place orders to limit mobility and transportation were among the strategic measures taken to control the rapid spreading of COVID-19. In major metropolitan areas, there was an estimated decrease of 50 to 90 percent in transit use. The secondary effect of the COVID-19 lockdown was expected to improve air quality, leading to a decrease in respiratory diseases. The present study examines the impact of mobility on air quality during the COVID-19 lockdown in the state of Mississippi (MS), USA. The study region is selected because of its non-metropolitan and non-industrial settings. Concentrations of air pollutants-particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), ozone (O3), nitrogen oxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO)-were collected from the Environmental Protection Agency, USA from 2011 to 2020. Because of limitations in the data availability, the air quality data of Jackson, MS were assumed to be representative of the entire region of the state. Weather data (temperature, humidity, pressure, precipitation, wind speed, and wind direction) were collected from the National Oceanic and Atmospheric Administration, USA. Traffic-related data (transit) were taken from Google for the year 2020. The statistical and machine learning tools of R Studio were used on the data to study the changes in air quality, if any, during the lockdown period. Weather-normalized machine learning modeling simulating business-as-scenario (BAU) predicted a significant difference in the means of the observed and predicted values for NO2, O3, and CO (p < 0.05). Due to the lockdown, the mean concentrations decreased for NO2 and CO by -4.1 ppb and -0.088 ppm, respectively, while it increased for O3 by 0.002 ppm. The observed and predicted air quality results agree with the observed decrease in transit by -50.5% as a percentage change of the baseline, and the observed decrease in the prevalence rate of asthma in MS during the lockdown. This study demonstrates the validity and use of simple, easy, and versatile analytical tools to assist policymakers with estimating changes in air quality in situations of a pandemic or natural hazards, and to take measures for mitigating if the deterioration of air quality is detected.

Transitional behaviors of fractional dimensional models of field-induced and space-charge limited emission from rough surfaces

Electron emission is an important physical phenomenon in both vacuum and solid-state devices. Previously, the effects of surface roughness or interface irregularity have been incorporated in the fractional Fowler–Nordheim (FNα) equation for field emission, the fractional Child–Langmuir (CLα) law for space-charge limited emission (SCLE) in vacuum, and the fractional Mott–Gurney (MGα) law for SCLE in the presence of carrier collisions using a fractional dimensional approach, where the fractional dimensional parameter (0&amp;lt;α≤1) defines the degree of roughness at electrode surface or irregularity of the gap medium. Here, the transitions between FNα, CLα, and MGα regimes have been investigated for electron emission from rough surfaces. It is shown that the fractional second-order or third-order nexuses, where at least two or three of the FNα, CLα, and MGα regimes converge, respectively, have a strong dependence on the surface conditions as captured by the parameter α. This work provides an important insight into the mechanism of electron emission in electron emitters where surface roughness is inevitable due to practical experimental fabrication and/or emitter surface degradation over time. The model shall serve as a versatile analytical tool to characterize the roughness by fitting the model to the experimental measurements.

Sharp Branched Gold Nanostar-Based Lateral-Flow Immunoassay for Detection of Yersinia pestis

Over the past few decades, colorimetric paper-based lateral flow immunoassay (LFIA) has emerged as a versatile analytical tool for rapid point-of-care detection of infectious diseases with high simplicity and flexibility. The LFIA sensitivity is based on color visualization of the antibody-labeled nanoparticles bound with the target analytes at the test line. Therefore, the nanoparticle design is crucial for LFIA sensitivity. The traditional LFIA is based on spherical gold nanoparticles, which usually suffer from poor sensitivity because of very low optical contrast at the test line. To improve the LFIA sensitivity, we have developed an LFIA based on gold nanostars (GNSs) with different branch lengths and sharpness (GNS-1, GNS-2, and GNS-3), which possess higher optical contrast than conventional gold nanospheres (GNSPs). We have selected the bacterium Yersinia pestis as a model analyte system. The effective affinity of GNSPs and GNSs with the Y. pestis fraction 1 (F1) protein was quantitively investigated by colorimetric and optical density measurements of the test line. The results show that GNS-3, which has maximum spike length and branch sharpness, exhibits the highest analytical sensitivity based on the limit of detection of the LFIA readout compared to other GNSs and GNSPs. The detection limit of the Y. pestis F1 antigen was achieved up to 0.1 ng/mL for GNS-3, which is 100 times lower than that for the GNSP at a 1 pmol/L concentration and 10 times lower than that for the reported procedure based on traditional gold nanoparticles. Overall, our prototype LFIA platform based on a highly spiked GNS (GNS-3) exhibits high analytical sensitivity, indicating it to be a promising candidate for routine LFIA application to detect infectious diseases.

LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies.

Recent advances in highly multiplexed imaging have provided unprecedented insights into the complex cellular organization of tissues, with many applications in translational medicine. However, downstream analyses of multiplexed imaging data face several technical limitations, and although some computational methods and bioinformatics tools are available, deciphering the complex spatial organization of cellular ecosystems remains a challenging problem. To mitigate this problem, we develop a novel computational tool, LOCATOR (anaLysis Of CAncer Tissue micrOenviRonment), for spatial analysis of cancer tissue microenvironments using data acquired from mass cytometry imaging technologies. LOCATOR introduces a graph-based representation of tissue images to describe features of the cellular organization and deploys downstream analysis and visualization utilities that can be used for data-driven patient-risk stratification. Our case studies using mass cytometry imaging data from two well-annotated breast cancer cohorts re-confirmed that the spatial organization of the tumour-immune microenvironment is strongly associated with the clinical outcome in breast cancer. In addition, we report interesting potential associations between the spatial organization of macrophages and patients' survival. Our work introduces an automated and versatile analysis tool for mass cytometry imaging data with many applications in future cancer research projects. Datasets and codes of LOCATOR are publicly available at https://github.com/RezvanEhsani/LOCATOR.

(Invited) Accelerating Electrochemistry: The Development of Rapid Impedance Methods and High-Throughput Screening of Novel Oxide Electrodes for Fuel Cells and Electrolyzers

As electrochemical technologies rise to ever greater prominence in the global energy landscape, new techniques to accelerate the discovery, design, and characterization of novel electrochemical materials and devices are needed. Motivated by this opportunity, here we present our recent work in the development of novel rapid multi-dimensional impedance characterization using the Distribution of Relaxation Times (DRT) combined with high-throughput impedance-based screening of electrocatalysts for ceramic fuel cells and electrolyzers, both of which offer opportunities to accelerate electrochemical materials research and development.The DRT has been established as a versatile tool for analysis of electrochemical impedance spectroscopy (EIS) data, providing insight into the relevant electrochemical processes of fuel cells, batteries, and other devices without the constraints of an a priori model. Here, we present a method for obtaining the DRT directly from rapid time-domain measurements. This technique provides the DRT on a time-scale that is 1-2 orders of magnitude faster than conventional EIS measurements, and opens new avenues for characterization and analysis that are inaccessible and/or impractical with conventional EIS. We demonstrate how this technique can be used to construct multidimensional DRT maps as a function of variables such as applied bias, temperature, changing gas conditions, or state-of-charge (Figure 1). In addition, the model can be adapted to account for time-varying sample states, enabling meaningful analysis of unstable samples and transient phenomena such as equilibration or degradation processes. Finally, we demonstrate how hardware sampling-rate limitations can be overcome via hybrid frequency- and time-domain measurements to scan a broad range of timescales without sacrificing measurement speed. The capability of this method to resolve the DRT along additional dimensions promises to enhance the interpretability of the DRT and provide new insight to guide materials and device optimization.We then discuss how rapid impedance characterization and analysis methods can be coupled to combinatorial thin-film synthesis and high-throughput automated characterization to investigate a large family of catalytically-active triple-ionic-electronic conducting oxide perovskite materials based on the Ba(Co,Fe,Zr,Y)O3-δ (BCFZY) compositional system, which show great promise for catalyzing the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) for ceramic fuel cells and electrolyzers (Figure 2). In total, we collected and analyzed more than 2,500 impedance spectra from three combinatorial thin-film electrode libraries, comprising 432 distinct compositions. These libraries were synthesized by pulsed laser deposition and measured at three temperatures under two different gas atmospheres, enabling a new scientific insight into the trends governing electrochemical performance. Our combinatorial experiments demonstrate that Co-rich compositions achieve the lowest overall polarization resistance under both dry air and humid N2, while high Fe content may impede the performance at low-to-intermediate temperatures. Reuslts from the combinatorial experiments are supported by isotope-labled SIMS trace diffusion studies as well as by protonic-ceramic fuel cell and electrolysis cell testing of both symmetric cells and full cells that incorporated select compositions of interest. Hierarchical Bayesian analysis indicates that the performance-limiting process depends on the chemical composition, measurement temperature, and atmospheric humidity. Thus, by combining rapid analysis methods with combinatorial experimentation, we achieve a composition map of the condition-dependent electronic properties of materials in the BCFZY perovskite family for application as air electrodes in protonic ceramic fuel cell and electrolyzers. Figure 1

In Situ Time-of-Flight Mass Spectrometry of Ionic Fragments Induced by Focused Electron Beam Irradiation: Investigation of Electron Driven Surface Chemistry inside an SEM under High Vacuum.

Recent developments in nanoprinting using focused electron beams have created a need to develop analysis methods for the products of electron-induced fragmentation of different metalorganic compounds. The original approach used here is termed focused-electron-beam-induced mass spectrometry (FEBiMS). FEBiMS enables the investigation of the fragmentation of electron-sensitive materials during irradiation within the typical primary electron beam energy range of a scanning electron microscope (0.5 to 30 keV) and high vacuum range. The method combines a typical scanning electron microscope with an ion-extractor-coupled mass spectrometer setup collecting the charged fragments generated by the focused electron beam when impinging on the substrate material. The FEBiMS of fragments obtained during 10 keV electron irradiation of grains of silver and copper carboxylates and shows that the carboxylate ligand dissociates into many smaller volatile fragments. Furthermore, in situ FEBiMS was performed on carbonyls of ruthenium (solid) and during electron-beam-induced deposition, using tungsten carbonyl (inserted via a gas injection system). Loss of carbonyl ligands was identified as the main channel of dissociation for electron irradiation of these carbonyl compounds. The presented results clearly indicate that FEBiMS analysis can be expanded to organic, inorganic, and metal organic materials used in resist lithography, ice (cryo-)lithography, and focused-electron-beam-induced deposition and becomes, thus, a valuable versatile analysis tool to study both fundamental and process parameters in these nanotechnology fields.

Infrared Spectroscopy–Quo Vadis?

Given the exquisite capability of direct, non-destructive label-free sensing of molecular transitions, IR spectroscopy has become a ubiquitous and versatile analytical tool. IR application scenarios range from industrial manufacturing processes, surveillance tasks and environmental monitoring to elaborate evaluation of (bio)medical samples. Given recent developments in associated fields, IR spectroscopic devices increasingly evolve into reliable and robust tools for quality control purposes, for rapid analysis within at-line, in-line or on-line processes, and even for bed-side monitoring of patient health indicators. With the opportunity to guide light at or within dedicated optical structures, remote sensing as well as high-throughput sensing scenarios are being addressed by appropriate IR methodologies. In the present focused article, selected perspectives on future directions for IR spectroscopic tools and their applications are discussed. These visions are accompanied by a short introduction to the historic development, current trends, and emerging technological opportunities guiding the future path IR spectroscopy may take. Highlighted state-of-the art implementations along with novel concepts enhancing the performance of IR sensors are presented together with cutting-edge developments in related fields that drive IR spectroscopy forward in its role as a versatile analytical technology with a bright past and an even brighter future.

Photon/spin echo in a Fabry-Perot cavity.

The pulse area theorem is a well-known versatile analytical tool for capturing the general nonlinear nature of light propagation in a two-level medium. Here we derive the pulse area theorem for the photon/spin echo signal generated in a one-side cavity. The obtained analytical solutions for primary and secondary echo pulse areas allow us to describe the nonlinear patterns of the photon/spin echo signals in an atomic ensemble in a cavity. The developed approach and the obtained results constitute an important step in the study of the general properties of the photon/spin echo in optical and microwave cavities expanding applications of the photon echo in coherent spectroscopy, quantum memory, and processing.

You cannot fight the pressure: Structural rearrangements of active pharmaceutical ingredients under magic angle spinning.

Although solid-state nuclear magnetic resonance (NMR) is a versatile analytical tool to study polymorphs and phase transitions of pharmaceutical molecules and products, this work summarizes examples of spontaneous and unexpected (and unwanted) structural rearrangements and phase transitions (amorphous-to-crystalline and crystalline-to-crystalline) under magic angle spinning (MAS) conditions, some of them clearly being due to the pressure experienced by the samples. It is widely known that such changes can often be detected by X-ray powder diffraction (XRPD); here, the capability of solid-state NMR experiments with a special focus on 1 H-13 C frequency-switched Lee-Goldburg heteronuclear correlation (FSLG HETCOR)/MAS NMR experiments to detect even subtle changes on a molecular level not observable by conventional 1D NMR experiments or XRPD is presented. Furthermore, it is shown that a polymorphic impurity combined with MAS can induce a crystalline-to-crystalline phase transition. This showcases that solid-state NMR is not always noninvasive and such changes upon MAS should be considered in particular when compounds are studied over longer time spans.