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At least 631 records · Page 35

Chemometrics and visible diffuse reflectance spectroscopy to classify plutonium dioxide

Diffuse reflectance (DR) spectra in the Vis-NIR (∼380–1050 nm) region were acquired for a series of PuO 2 samples with a spot size of about 10 × 10 μm. Two batches of six PuO 2 samples, synthesized approximately 7.5 months apart, were prepared using both Pu(III) and Pu(IV) oxalate precursors at three distinct calcination temperatures (450, 650, and 950 °C). This yielded a total of 12 PuO 2 samples and 433 DR spectra. The DR spectrum of PuO 2 contained numerous peaks in the visible region, and characteristic features were identified with respect to calcination temperature and chemistry. A distinct peak multiplet near 615 nm was observed for samples prepared at low calcination temperatures, and a peak near 660 nm was observed for higher calcination temperatures. A multivariate classification strategy based on principal component analysis (PCA) was developed to distinguish PuO 2 calcination temperatures of 450, 650, and 950 °C with 100 % accuracy. Classification results also indicate the potential to distinguish chemical processing history (i.e., Pu(III) or Pu(IV)) based on the spectra with 72 % accuracy based on k-nearest neighbors applied to the PCA scores. Partial least squares discriminant analysis was used to identify variation among batches with 88 % accuracy and found that peaks near 669, 681, 811, and 970 nm were the most useful for predicting the batch identity. Here, this work demonstrates how micro-diffuse reflectance spectroscopy and chemometrics can be used to classify PuO 2 processing history based on Vis-NIR spectral features. Combining the chemometric approach with mapping sequences could provide a rapid, nondestructive approach to classify Pu oxide materials for environmental, forensics, and nonproliferation applications.

Actinide↗

The concentration of BTEX in selected urban areas of Malaysia during the COVID-19 pandemic lockdown

Volatile organic compounds (VOCs) such as benzene, toluene, ethylbenzene and xylene (BTEX) are air pollutants that harm human health. This study aims to identify BTEX concentrations before the lockdown known as the Movement Control Order was imposed (BMCO), during the implementation of the Movement Control Order (MCO), and then during the Conditional Movement Control Order (CMCO). These orders were introduced during the COVID-19 pandemic in Malaysia. The study utilised data measured by the continuous monitoring of BTEX using online gas chromatography instruments located at three urban area stations. Here, the results showed that the BTEX concentrations reduced by between –38% and –46% during the MCO compared to the BMCO period. The reduction of human mobility during the MCO and CMCO influenced the lower BTEX concentrations recorded at a station within the Kuala Lumpur area. The results of the BTEX diagnostic ratios and principal component analysis showed that the major source of BTEX, especially during the BMCO and CMCO periods, was motor vehicle emissions. Further investigation, using correlation analysis and polar plots, showed that the BTEX concentrations were also influenced by meteorological variables such as wind speed, air temperature and relative humidity.

54 ENVIRONMENTAL SCIENCES↗

A new classification scheme of plastic wastes based upon recycling labels

Highlights: • PET, HPDE or PP types of plastics are considered. • An automated classification of plastic bottles based on the feature extraction and classification methods is performed. • The decision mechanism consists of PCA, Kernel PCA, FLDA, SVD and Laplacian Eigenmaps methods. • SVM is selected to achieve the classification task and majority voting technique is used. - Abstract: Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher’s Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple experimental setup with a camera and homogenous backlighting. Due to the giving global solution for a classification problem, Support Vector Machine (SVM) is selected to achieve the classification task and majority voting technique is used as the decision mechanism. This technique equally weights each classification result and assigns the given plastic object to the class that the most classification results agree on. The proposed classification scheme provides high accuracy rate, and also it is able to run in real-time applications. It can automatically classify the plastic bottle types with approximately 90% recognition accuracy. Besides this, the proposed methodology yields approximately 96% classification rate for the separation of PET or non-PET plastic types. It also gives 92% accuracy for the categorization of non-PET plastic types into HPDE or PP.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Database-wide hazard modelling of the onset of DIII-D tearing modes with field features

The rate of onset (hazard) of tearing modes is modelled probabilistically using statistical learning algorithms. Axisymmetric energy-density equilibrium fields are taken as raw high-dimensional input features which are reduced with principal component analysis. Signal processing of non-axisymmetric magnetics fluctuation array data provides the target information from which to learn. Model selection, visualization and calibration assessment procedures are detailed. Here, the analysis is deployed at large scale across the DIII-D tokamak database. Standard model selection criteria suggest that the energy-density post-processed feature is a better choice for modelling the onset rate compared to the non-processed equilibrium reconstruction solution. Two example applications of the learned rate function are demonstrated: (i) proximity-to-onset discharge monitoring and (ii) database analysis showing an (expected) observational global trend that the general hazard increases as a plasma performance metric increases. An important connection between the hazard function and its use as a conditional probability generator is reviewed in the Appendix.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

MALDI-Mass Spectrometric Imaging Revealing Hypoxia-Driven Lipids and Proteins in a Breast Tumor Model

Hypoxic areas are a common feature of rapidly growing malignant tumors and their metastases, and are typically spatially heterogeneous. Hypoxia has a strong impact on tumor cell biology and contributes to tumor progression in multiple ways. To date, only a few molecular key players in tumor hypoxia, such as for example hypoxia-inducible factor-1 (HIF-1), have been discovered. The distribution of biomolecules is frequently heterogeneous in the tumor volume, and may be driven by hypoxia and HIF-1α. Understanding the spatially heterogeneous hypoxic response of tumors is critical. Mass spectrometric imaging (MSI) provides a unique way of imaging biomolecular distributions in tissue sections with high spectral and spatial resolution. In this paper, breast tumor xenografts grown from MDA-MB-231-HRE-tdTomato cells, with a red fluorescent tdTomato protein construct under the control of a hypoxia response element (HRE)-containing promoter driven by HIF-1α, were used to detect the spatial distribution of hypoxic regions. We elucidated the 3D spatial relationship between hypoxic regions and the localization of small molecules, metabolites, lipids, and proteins by using principal component analysis – linear discriminant analysis (PCA-LDA) on 3D rendered MSI volume data from MDA-MB-231-HRE-tdTomato breast tumor xenografts. In this study we identified hypoxia-regulated proteins active in several distinct pathways such as glucose metabolism, regulation of actin cytoskeleton, protein folding, translation/ribosome, splicesome, the PI3K-Akt signaling pathway, hemoglobin chaperone, protein processing in endoplasmic reticulum, detoxification of reactive oxygen species, aurora B signaling/apoptotic execution phase, the RAS signaling pathway, the FAS signaling pathway/caspase cascade in apoptosis and telomere stress induced senescence. In parallel we also identified co-localization of hypoxic regions and various lipid species such as PC(16:0/18:1), PC(16:0/18:2), PC(18:0/18:1), PC(18:1/18:1), PC(18:1/18:2), PC(16:1/18:4), PC(18:0/20:3), PC(16:0/22:1), among others. Lastly, our findings shed light on the biomolecular composition of hypoxic tumor regions, which may be responsible for a given tumor’s resistance to radiation or chemotherapy.

60 APPLIED LIFE SCIENCES↗

Quantifying Temperature Dependence of Pu(IV) Absorbance Spectra for Advanced Online Monitoring of Nuclear Processes

This article presents a systematic study of Pu(IV) absorbance spectral features as a function of temperature to develop an understanding of this parameter’s effect on chemometric models that can be used as online monitoring tools to support nuclear processing. The descriptive and predictive models that provide real-time feedback of these processes are usually constructed with data collected in conditions typical of a laboratory environment, which can differ drastically from a processing environment. To assess the impact of temperature on Pu(IV) absorbance spectra, 11 samples of Pu(IV) were synthesized with varying HNO 3 concentrations ranging from 0.6 to 9.5 M and heated between 15 and 45 °C. Ultraviolet (UV)–visible (vis)–near-infrared (NIR) absorption spectra collected at different HNO 3 concentrations and temperatures revealed that features associated with Pu(IV) are sensitive to temperature at all HNO 3 concentrations and that changes in features depend on HNO 3 concentration. The contributions of temperature and HNO 3 concentration to variation in Pu(IV) spectral features were evaluated using the principal component analysis of spectra that were baseline-corrected with an asymmetric least-squares method. Furthermore, predictive modeling for HNO 3 concentration with partial least-squares regression of UV–vis–NIR spectra highlighted the importance of accounting for temperature in the calibration set to optimize model performance. This methodology constitutes a new, systematic approach to account for the effect of temperature on the absorption spectra of metal ions and is useful for process monitoring applications in many industries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Coupled X-ray Fluorescence and X-ray Absorption Spectroscopy for Microscale Imaging and Identification of Sulfur Species within Tissues and Skeletons of Scleractinian Corals

Identifying and mapping the wide range of sulfur species within complex matrices presents a challenge for understanding the distribution of these important biomolecules within environmental and biological systems. Here, we present a coupled micro X-ray fluorescence (μXRF) and X-ray absorption near-edge structure (XANES) spectroscopy method for determining the presence of specific sulfur species in coral tissues and skeletons at high spatial resolution. By using multiple energy stacks and principal component analysis of a large spectral database, we were able to more accurately identify sulfur species components and distinguish different species and distributions of sulfur formerly unresolved by previous studies. Specifically, coral tissues were dominated by more reduced sulfur species, such as glutathione disulfide, cysteine, and sulfoxide, as well as organic sulfate as represented by chondroitin sulfate. Sulfoxide distributions were visually correlated with the presence of zooxanthellae endosymbionts. Coral skeletons were composed primarily of carbonate-associated sulfate (CAS) along with minor contributions from organic sulfate and a separate inorganic sulfate likely in the form of adsorbed sulfate. In conclusion, this coupled XRF-XANES approach allows for a more accurate and informative view of sulfur within biological systems in situ and holds great promise for pairing with other techniques to allow for a more encompassing understanding of elemental distributions within the environment.

54 ENVIRONMENTAL SCIENCES↗

Ion Clusters Reveal the Sources, Impacts, and Drivers of Freshwater Salinization

Population growth, land use change, climate change, and natural resource extraction are driving the salinization of freshwater resources worldwide. Reversing these trends will require data-centric approaches that identify salt sources, environmental drivers, and ecosystem responses. In this study, we applied principal component analysis and hierarchical clustering to identify ion covariance patterns, or “ion clusters,” in Broad Run, an urban stream in the Mid-Atlantic United States. These clusters correspond to distinct hydrologic regimes and reveal specific salinization risks: (1) phosphorus pollution mobilized during summer storms (Cluster 1); (2) elevated concentrations of sulfate and bicarbonate during baseflow (Cluster 2), likely reflecting groundwater discharge; and (3) elevated specific conductance and sodium, chloride, and potassium ion concentrations during snowmelt and rain-on-snow events (Cluster 3), driven by deicer and anti-icer wash-off. These ion fingerprints offer a transferable framework for diagnosing salt sources, assessing ecological risk, and identifying management targets. Our findings underscore the need for next-generation stormwater infrastructure and smart growth policies to protect aquatic life in rapidly urbanizing watersheds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

In Situ Monitoring of Groundwater Contamination Using the Kalman Filter

This study presents a Kalman filter-based framework to establish a real-time in situ monitoring system for groundwater contamination based on in situ measurable water quality variables, such as specific conductance (SC) and pH. First, this framework uses principal component analysis (PCA) to identify correlations between the contaminant concentrations of interest and in situ measurable variables. It then applies the Kalman filter to estimate contaminant concentrations continuously and in real-time by coupling data-driven concentration-decay models with the previously identified data correlations. We demonstrate our approach with historical groundwater data from the Savannah River Site F-Area: We use SC and pH data to estimate tritium and uranium concentrations over time. Results show that the developed method can estimate these contaminant concentrations based on in situ measurable variables. The estimates remain reliable with less frequent or no direct measurements of the contaminant concentrations, while capturing the dynamics of short- and long-term contaminant concentration changes. In addition, we show that data mining, such as PCA, is useful to understand correlations in groundwater data and to design long-term monitoring systems. As a result, the developed in situ monitoring methodology is expected to improve long-term groundwater monitoring by continuously confirming the contaminant plume's stability and by providing an early warning system for unexpected changes in the plume's migration.

54 ENVIRONMENTAL SCIENCES↗

UV–Vis–NIR Reflectance Spectroscopy and Chemometrics for Monitoring Pu Directly on an Ion Exchange Column

Here, we present a fiber-optic UV–vis–NIR reflectance spectroscopy method for direct, noninvasive monitoring of Pu(IV) in a glass ion exchange column during dynamic loading and elution in a glovebox. A movable probe enables spatially resolved spectral acquisition along the column axis, capturing distinct features associated with Pu(IV) nitrate complexes during loading and free ions during elution. Principal component analysis was applied to extract the dominant spectral variance and resolve relative concentration profiles without requiring precise knowledge of optical penetration depth or species identity. This in situ approach reveals spatial gradients and speciation dynamics in real time, which provides actionable insight into Pu(IV) ion migration, resin saturation, and breakthrough behavior under evolving flow conditions. The method offers a practical, fiber-compatible strategy to monitor glass column–based separations for Pu and other lanthanides or actinides and to characterize metal–resin interactions in flow-through systems.

actinide↗

Extraction of Nitric Acid and Uranium with DEHiBA under High Loading Conditions

We report the mechanism by which high concentrations (1.5 M in n-dodecane) of N,N-di-2-ethylhexylisobutyramide (DEHiBA) extracts HNO 3 and UO 2 (NO 3 ) 2 is under examination. Most prior studies have examined the extractant and the mechanism at a concentration of 1.0 M in n-dodecane, however under the higher loading conditions that can be achieved by a higher concentration of extractant this mechanism could change. Increased extraction of both nitric acid and uranium is observed with an increased concentration of DEHiBA. The mechanisms are examined by thermodynamic modeling of distribution ratios, 15 N NMR, and FTIR spectroscopy coupled with Principal Component Analysis (PCA). Speciation diagrams produced through thermodynamic modeling have been qualitatively reproduced through PCA of the FTIR spectra. The predominant extracted species of HNO 3 (DEHiBA), HNO 3 (DEHiBA) 2 , and UO 2 (NO 3 ) 2 (DEHiBA) 2 are in good agreement with prior literature reports for 1.0 M DEHiBA systems. Evidence for an additional species of either UO 2 (NO 3 ) 2 (DEHiBA) or UO 2 (NO 3 ) 2 (DEHiBA) 2 (HNO 3 ) also contributing to the extraction of uranium species is given.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design

Computational catalyst design requires identification of a metal and ligand that together result in the desired reaction reactivity and/or selectivity. A major impediment to translating computational designs to experiments is evaluating ligands that are likely to be synthesized. Here we provide a solution to this impediment with our ReaLigands library that contains >30,000 monodentate, bidentate (didentate), tridentate, and larger ligands cultivated by dismantling experimentally reported crystal structures. Individual ligands from mononuclear crystal structures were identified using a modified depth-first search algorithm and charge was assigned using a machine learning model based on quantum-chemical calculated features. In the library ligands are sorted based on direct ligand-to-metal atomic connections and on denticity. Representative principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) analyses were used to analyze several tridentate ligand categories, which revealed both the diversity of ligands and connections between ligand categories. Furthermore, we also demonstrated the utility of this library by implementing it with our building and optimization tools, which resulted in the very rapid generation of barriers for 750 bidentate ligands for Rh-hydride ethylene migratory insertion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A Distal Disulfide Bridge in OXA-1 β-Lactamase Stabilizes the Catalytic Center and Alters the Dynamics of the Specificity Determining Ω Loop

Widespread antibiotic resistance, particularly when mediated by broad-spectrum β-lactamases, has major implications for public health. Substitutions in the active site often allow broad-spectrum enzymes to accommodate diverse types of β-lactams. Substitutions observed outside the active site are thought to compensate for the loss of thermal stability. The OXA-1 clade of class D β-lactamases contains a pair of conserved cysteines located outside the active site that forms a disulfide bond in the periplasm. In this paper, the effect of the distal disulfide bond on the structure and dynamics of OXA-1 was investigated via 4 μs molecular dynamics simulations. The results reveal that the disulfide promotes the preorganized orientation of the catalytic residues and affects the conformation of the functionally important Ω loop. Furthermore, principal component analysis reveals differences in the global dynamics between the oxidized and reduced forms, especially in the motions involving the Ω loop. A dynamical network analysis indicates that, in the oxidized form, in addition to its role in ligand binding, the KTG family motif is a central hub of the global dynamics. Finally, as activity of OXA-1 has been measured only in the reduced form, we suggest that accurate assessment of its functional profile would require oxidative conditions mimicking periplasm.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Exploring Responses of Contact Kelvin Probe Force Microscopy in Triple-Cation Double-Halide Perovskites

Hybrid organic–inorganic perovskite (HOIP) solar cells have undergone unprecedentedly rapid improvement in power conversion efficiencies during the last decade, but controversy remains regarding the role of local phenomena and heterogeneity in grain by grain. These challenges in turn necessitate understanding the fundamental mechanisms operating at individual grains and exploring how these mechanisms give rise to the collective behaviors of the films. Although numerous efforts have targeted local phenomena in these materials using classical scanning probe microscopy modalities, the measurements remain difficult to interpret due to intrinsic coupling between possible ferroelectric, ion migration, bulk and surface electrochemical phenomena, and the effects of light on these behaviors. Here, we explore the electromechanical responses in (Cs 0.05 FA 0.85 MA 0.15 )Pb(I 0.85 Br 0.15 ) 3 perovskite thin film in the dark and under illumination using band excitation-piezoresponse force microscopy (BE-PFM). To identify and separate ferroic, electrostatic, and electrochemical responses, we utilize contact-Kelvin probe force microscopy (cKPFM). The cKPFM responses show weak variability with morphological features in the dark, while such variability significantly increases under illumination. Deconvolution of the cKPFM responses via principal component analysis (PCA) shows heterogeneity in grain to grain possibly due to variation in migration and segregation of halide ions in mixed perovskites at specific grains. Further, we propose an electrostatic mechanism based on the physics of semiconductors, which can directly link the observed cKPFM behaviors with the ionic and electronic density of states (DOS) and their time dynamics. Here, these findings suggest that cKPFM is a powerful probe of local ionic behaviors on the nanoscale, both in HOIPs and other materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Unraveling Fundamental Activity–Stability Relationships in Rutile Oxides

The oxygen evolution reaction (OER) is a key anodic half-cell reaction that accompanies several critical electrochemical reduction reactions of interest to a variety of applications. Despite steady advances in understanding and qualitatively predicting OER activity and selectivity trends, a comprehensive description or prediction of material aqueous (in)stability and degradation mechanisms remains elusive, even though these processes critically influence device lifetime and economic feasibility. In this work, we investigate the interplay, or lack thereof, between OER activity and material aqueous stability across rutile oxides, with a particular focus on iridium oxide (IrO 2 ). By applying a Born–Haber cycle, we calculate the thermodynamic driving force for metal dissolution as a function of the applied bias and electrolyte conditions. We apply interpretable machine learning techniques, including principal component analysis and symbolic regression, to analyze trends across rutile oxides and find that key thermodynamic descriptors for OER activity and surface stability are only very weakly correlated. Instead, the local atomic environment─especially electronic structure signatures for interactions between the active site and its neighbors─plays a more important role in predicting material stability. Leveraging these insights, we investigate the impact of doping IrO 2 with a range of transition metals and show that the stability of Ir active sites can be tuned largely independently of its predicted OER activity. These insights lay the foundation for material design to improve stability with respect to corrosion, with the ultimate aim to enhance long-term stability without sacrificing catalytic performance in the OER.

evolution reactions↗

Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning

Computational catalyst screening has the potential to significantly accelerate heterogeneous catalyst discovery. Typically, this involves developing microkinetic reactor models that are based on parameters obtained from density functional theory and transition-state theory. To reduce the large computational cost involved in computing various adsorption and transition-state energies of all possible surface states on a large number of catalyst models, linear scaling relations for surface intermediates and transition states have been developed that only depend on a few, typically one or two descriptors, such as the carbon atom adsorption energy. As a result, only the descriptor values have to be computed for various active site models to generate volcano curves in activity or selectivity. Unfortunately, for more complex chemistries the predictability of linear scaling relations is unknown. Also, the selection of descriptors is essentially a trial and error process. Here, using a database of adsorption energies of the surface species involved in the decarboxylation and decarbonylation of propionic acid over eight monometalic transition-metal catalyst surfaces (Ni, Pt, Pd, Ru, Rh, Re, Cu, Ag), we tested if nonlinear machine learning (ML) models can outperform the linear scaling relations in prediction accuracy when predicting the adsorption energy for various species on a metal surface based on data from the rest of the metal surfaces. We found linear scaling relations to hold well for predictions across metals with a mean-absolute error of 0.12 eV, and ML methods being unable to outperform linear scaling relations when the training dataset contains a complete set of energies for all of the species on various metal surfaces. Only when the training dataset is incomplete, namely, contains a random subset of species’ energies for each metal, a currently unlikely scenario for catalyst screening, do kernel-based ML models significantly outperform linear scaling relations. We also found that simple coordinate-free species descriptors, such as bond counts, achieve as good results as sophisticated coordinate-based descriptors. Finally, we propose an approach for automatic discovery of appropriate metal descriptors using principal component analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Detecting DNA Methylation Using Surface-Enhanced Raman Spectroscopy

Among all of the epigenetic events that are responsible for various diseases such as cancer, lupus, and several birth defects, DNA methylation is one of the crucial ones. It occurs because of the alkylation of various bases. These modifications of genes carried through altered creations of proteins ultimately lead to various diseases. For example, a vital cause behind canine lymphoma is found to be DNA methylation in the guanine base. In this work, we analyzed the methylated and nonmethylated guanine structure with the assistance of surface-enhanced Raman spectroscopy and density functional theory (DFT). Because of their vulnerability for causing DNA methylation, the N7 and O6 positions of the guanine structure were the positions of interest with the addition of various adducts such as methyl, hydroxyethyl, and deuterated methyl groups. To distinguish the methylated samples from the nonmethylated ones, principal component analysis was performed, and the same analysis was used to distinguish their methylated positions and added adducts. The experimental results were then explained by structure optimization and frequency calculation of the molecules based on DFT calculations. To understand the charge distribution and detect the possible locations of alkylation of DNA bases, electrostatic potential, highest occupied molecular orbital, and lowest unoccupied molecular orbital for each of the molecules were analyzed, and the reactivity was discussed in the light of electronic structure calculations. The results presented in this study demonstrate a potential label-free technique to examine epigenetic modification of DNA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗