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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Measuring Plant Metabolite Abundance in Spearmint ( Mentha spicata L.) with Raman Spectra to Determine Optimal Harvest Time

A fast field-deployable method utilizing Raman spectroscopy to determine the optimal harvest time of plants to extract the highest abundance of target metabolites is presented. Rosmarinic acid is a metabolite extracted from spearmint (Mentha spicata L.). Leaves from commercial “Native” and proprietary clonal line “KI110” spearmint were measured as a function of cell type and age to determine rosmarinic acid abundance. A linear regression model with leave-one-out cross-validation (R 2 CV = 0.61, RMSECV = 11.1 mg/g) was developed between selected Raman peak areas and rosmarinic acid concentrations determined by high-performance liquid chromatography (HPLC). A principal component analysis (PCA) model was also developed to determine rosmarinic acid abundance. The method may be suited to the analysis of many agriculturally relevant plant species and metabolites with distinct Raman peaks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Topological Data Analysis for Particulate Gels

Soft gels, formed via the self-assembly of particulate materials, exhibit intricate multiscale structures that provide them with flexibility and resilience when subjected to external stresses. Here, this work combines particle simulations and topological data analysis (TDA) to characterize the complex multiscale structure of soft gels. Our TDA analysis focuses on the use of the Euler characteristic, which is an interpretable and computationally scalable topological descriptor that is combined with filtration operations to obtain information on the geometric (local) and topological (global) structure of soft gels. We reduce the topological information obtained with TDA using principal component analysis (PCA) and show that this provides an informative low-dimensional representation of the gel structure. We use the proposed computational framework to investigate the influence of gel preparation (e.g., quench rate, volume fraction) on soft gel structure and to explore dynamic deformations that emerge under oscillatory shear in various response regimes (linear, nonlinear, and flow). Our analysis provides evidence of the existence of hierarchical structures in soft gels, which are not easily identifiable otherwise. Moreover, our analysis reveals direct correlations between topological changes of the gel structure under deformation and mechanical phenomena distinctive of gel materials, such as stiffening and yielding. In summary, we show that TDA facilitates the mathematical representation, quantification, and analysis of soft gel structures, extending traditional network analysis methods to capture both local and global organization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Insights into Tetravalent Np Speciation in HNO 3 through Spectroelectrochemistry and Multivariate Analysis

In situ optical spectroscopy, spectropotentiometry, and multivariate analysis were applied to the Np(IV) nitrate system to better understand speciation and quantify HNO 3 concentration. Thin-layer spectropotentiometry, or spectroelectrochemistry, was leveraged to isolate and stabilize Np(IV) without compromising the solution conditions and generate representative Vis-NIR absorption spectra from 0.5 to 10 M HNO 3 and benchmark the corresponding Np(IV) molar absorptivity coefficients. Spectra were described with principal component analysis (PCA) to identify the purest Np(IV) absorbance spectra among other oxidation states [e.g., Np(V/VI)] at each acid concentration and then to identify the primary sources of variance within each Np(IV) spectrum with respect to Np(IV) nitrate complexes. Then, partial least-squares regression (PLSR) and support vector regression (SVR) models were built to predict HNO 3 concentration from the Np(IV) spectral data. The nonlinear SVR model outperformed the linear PLSR model for the HNO 3 concentration predictions. Finally, the inclusion of spectra collected in edge and center point HNO 3 concentrations in the calibration set was determined to be crucial for producing models with strong predictive capabilities. The multivariate approach used in this study makes it possible to quantify HNO 3 concentration solely based on Np(IV) absorption spectra, which is essential to quantifying processing streams in various online monitoring applications.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2026

Novel Cell-Type-Specific Drought-Responsive Proteins in Root Tips of Field-Grown Perennial Switchgrass

The root-tip region of plants, including the root cap, forms the most basal terminal of the root and exhibits a high degree of cellular complexity in terms of morphology, cytological function, and interaction with environmental cues in the soil. Cells in this region follow a developmental trajectory, transitioning from stem cells to meristematic cells, and ultimately to fully differentiated cell types. However, our understanding of root-tip cell-type specific proteomic responses to abiotic stresses, such as drought, particularly under field conditions, remains limited. This study aimed to identify spatially resolved, cell type-specific proteomes in switchgrass (Panicum virgatum) root tips under drought stress. Root tips were collected from seven-year-old, field-grown switchgrass ‘Alamo’ plants excavated under both well-watered and long-term drought conditions. Cell type-specific proteins were identified using laser capture microdissection (LCM) coupled with nanoPOTS (Nanodroplet Processing in One Pot for Trace Samples) and nano-LC-MS proteomics analysis. Five distinct cell types were targeted: (1) cells in the quiescent center and stem cell niche (QuC), (2) protodermal epidermal cells (PEC) in the meristematic zone, (3) epidermal cells in the transition and elongation zones above the root cap (Epi), (4) peripheral root cap cells (PRC), forming 2–3 layers below the PEC and 1–2 layers above the root border cells, and (5) columella root cap cells (Col) comprising of the columella initials and a single underlying layer of cells undergoing active growth. Principal component analysis (PCA) revealed clear separation among the five targeted cell types, confirming distinct proteomic profiles. Proteins predominantly enriched in each cell type were linked to distinct cellular functions, with QuC cells showing involvement in chromosomal behavior, DNA replication, and mitosis—key processes for stem cell niche regulation. Drought stress resulted in alterations of proteostasis, as evidenced by significant decreases in ribosomal proteins and increases in protein synthesis inhibitors. Moreover, drought stress induced unique cell-type–specific proteins involved in phytohormone biosynthesis and signaling pathways, including auxin, cytokinin, and jasmonic acid. In particular, QuC cells were more highly enriched in proteins associated with DNA repair and mitotic processes. Metabolic pathways related to amino acids, carbohydrates, and lipids were differentially affected in a cell-type–dependent manner, whereas general stress-responsive proteins exhibited consistent changes across all five cell types. Overall, this study provides unique spatially resolved, cell-type-specific proteomic profiles in root tips, representing a significant advancement in our understanding of the cellular mechanisms underlying plant responses to drought stress in natural field conditions.

perennial grass↗
DOE OSTI2020

Investigation of a Lignin-Based Deep Eutectic Solvent Using p -Hydroxybenzoic Acid for Efficient Woody Biomass Conversion

Deep eutectic solvents (DESs) are effective solvents for biomass conversion and have been proposed as alternatives to ionic liquids (ILs). In this paper, we first report an effective pretreatment of woody biomass using a novel hardwood lignin-based DES prepared from p -hydroxybenzoic acid (PB) and choline chloride (ChCl), which are used as the hydrogen bond donor and acceptor, respectively. The impacts of the ChCl-PB DES on structural properties and enzymatic hydrolysis of poplar wood were comprehensively evaluated. The performance of ChCl-PB DES was also compared with two other DESs with p -coumaric acid (PCA) and 4-hydroxybenzaldehyde (PHA) as the hydrogen bond donors, which have been successfully applied to herbaceous biomass conversion in previous studies. The results showed that hemicellulose and lignin were effectively fractionated during the proposed ChCl-PB DES pretreatment under a relatively mild temperature. The ChCl-PB DES pretreatment resulted in up to 69% of delignification, and the pretreated poplar residues had over 90% of glucose yield by a 72 h-enzymatic hydrolysis. Transformation of poplar biomass during the DES pretreatment was evaluated by gel permeation chromatography (GPC), two-dimensional heteronuclear single quantum coherence (2D-HSQC), and 31 P nuclear magnetic resonance (NMR) spectroscopies. The recovered lignin showed relatively high purity, narrow molecular weight distribution (PDI < 1.6), and relatively low molecular weight (~1400 g/mol for M w ). A sustainable process was achieved by recycling DES as well as the utilization of PB, which is a producible aromatic compound from hardwood lignin, for the proposed DES formation in the system. This study indicates that the proposed novel DES with a hardwood lignin-based PB compound is a promising pretreatment solvent to achieve an economically feasible biomass conversion process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Accurate Thermochemistry of Complex Lignin Structures via Density Functional Theory, Group Additivity, and Machine Learning

A molecular-level understanding of lignin structures and bond dissociation energies could facilitate depolymerization technologies. Still, this information is currently limited due to the lack of databases and the simplification of surrogate models. Here, substitution effects on seven common linkages in lignin polymers are systematically investigated. An automated reaction network generator is employed to create a database of structures. A new group additivity (GA) model based on principal component analysis (PCA) descriptors is introduced and trained on gas-phase density functional theory data of 4100 species at the M06-2X/6-311++G(d,p) level. Hydrogen bonds, local steric, and nonaromatic ring contributions are also incorporated. Lastly, we improve the accuracy of the group additivity model to reach the G4 theory by computing a data set of 770 species at this level and using a data fusion approach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Covalent Drug Binding in Live Cells Monitored by Mid-Infrared Quantum Cascade Laser Spectroscopy: Photoactive Yellow Protein as a Model System

The detection of drug-target interactions in live cells enables analysis of therapeutic compounds in a native cellular environment. Recent advances in spectroscopy and molecular biology have facilitated the development of genetically encoded vibrational probes like nitriles that can sensitively report on molecular interactions. Nitriles are powerful tools for measuring electrostatic environments within condensed media like proteins, but such measurements in live cells have been hindered by low signal-to-noise ratios. In this study, we design a spectrometer based on a double-beam quantum cascade laser (QCL)-based transmission infrared (IR) source with balanced detection that can significantly enhance sensitivity to nitrile vibrational probes embedded in proteins within cells compared to a conventional FTIR spectrometer. Here, using this approach, we detect small-molecule binding in Escherichia coli, with particular focus on the interaction between para-Coumaric acid (pCA) and nitrile-incorporated photoactive yellow protein (PYP). This system effectively serves as a model for investigating covalent drug binding in a cellular environment. Notably, we observe large spectral shifts of up to 15 cm –1 for nitriles embedded in PYP between the unbound and drug-bound states directly within bacteria, in agreement with observations for purified proteins. Such large spectral shifts are ascribed to the changes in the hydrogen-bonding environment around the local environment of nitriles, accurately modeled through high-level molecular dynamics simulations using the AMOEBA force field. Our findings underscore the QCL spectrometer’s ability to enhance sensitivity for monitoring drug–protein interactions, offering new opportunities for advanced methodologies in drug development and biochemical research.

chromophores↗
DOE OSTI2021

PyKrev: A Python Library for the Analysis of Complex Mixture FT-MS Data

In this study, we present PyKrev, a Python library for the analysis of complex mixture Fourier transform mass spectrometry (FT-MS) data. PyKrev is a comprehensive suite of tools for analysis and visualization of FT-MS data after formula assignment has been performed. These comprise formula manipulation and calculation of chemical properties, intersection analysis between multiple lists of formulas, calculation of chemical diversity, assignment of compound classes to formulas, multivariate analysis, and a variety of visualization tools producing van Krevelen diagrams, class histograms, PCA score, and loading plots, biplots, scree plots, and UpSet plots. The library is showcased through analysis of hot water green tea extracts and Scotch whisky FT-ion cyclotron resonance-MS data sets. PyKrev addresses the lack of a single, cohesive toolset for researchers to perform FT-MS analysis in the Python programming environment encompassing the most recent data analysis techniques used in the field.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Predictive Inverse Model for Advective Heat Transfer in a Short–Circuited Fracture: Dimensional Analysis, Machine Learning, and Field Demonstration

Identifying fluid flow maldistribution in planar geometries is a well–established problem in subsurface science/engineering. Of particular importance to the thermal performance of enhanced (or “engineered”) geothermal systems is identifying the existence of nonuniform (i.e., heterogeneous) permeability and subsequently predicting advective heat transfer. Here, machine learning via a genetic algorithm (GA) identifies the spatial distribution of an unknown permeability field in a two–dimensional Hele–Shaw geometry (i.e., parallel plates). The inverse problem is solved by minimizing the L2 norm between simulated residence time distribution (RTD) and measurements of an inert tracer breakthrough curve (BTC) (C–Dot nanoparticle). Principal component analysis (PCA) of spatially correlated permeability fields enabled reduction of the parameter space by more than a factor of 10 and restricted the inverse search to reservoir–scale permeability variations. Thermal experiments and tracer tests conducted at the mesoscale Altona Field Laboratory (AFL) demonstrate that the method accurately predicts the effects of extreme flow channeling on heat transfer in a single bedding–plane rock fracture. However, this is only true when the permeability distributions provide adequate matches to both tracer RTD and frictional pressure loss. Without good agreement to frictional pressure loss, it is still possible to match a simulated RTD to measurements, but subsequent predictions of heat transfer are grossly inaccurate. Here, the results of this study suggest that it is possible to anticipate the thermal effects of flow maldistribution, but only if both simulated RTDs and frictional pressure loss between fluid inlets and outlets are in good agreement with measurements.

42 ENGINEERING↗
DOE OSTI2021

Predicting Thermal Performance of an Enhanced Geothermal System From Tracer Tests in a Data Assimilation Framework

Abstract Predicting the thermal performance of an enhanced geothermal system (EGS) requires a comprehensive characterization of the underlying fracture flow patterns from practically available data such as tracer data. However, due to the inherent complexities of subsurface fractures and the generally insufficient geological/geophysical data, interpreting tracer data for fracture flow characterization and thermal prediction remains a challenging task. The present study aims to tackle the challenge by leveraging a data assimilation method to maximize the utilization of information inherently contained in tracer data, and meanwhile maintain the flexibility to handle various uncertainties. A tracer data interpretation framework was proposed with the following three components integrated: (a) We use principal component analysis (PCA) to reduce the dimensionality of model parameter space. (b) We use ES‐MDA (ensemble smoother with multiple data assimilation) to invert for fracture aperture/flow fields and obtain posterior model ensembles for uncertainty quantification. Various data types are assimilated jointly to improve the predictive ability of the posterior ensemble. (c) The inverted fracture aperture fields are then incorporated into reservoir models to predict thermal performance. We developed a field‐scale EGS model to verify the ability of the framework to characterize highly heterogeneous fracture aperture/flow fields and predicting thermal performance. We also applied the framework to a mesoscale field experiment to demonstrate its potential application in real‐world geothermal reservoirs. The results indicate that the proposed framework can effectively retrieve fracture flow information from tracer data for thermal prediction and uncertainty quantification, and thus provide informative guidance for EGS optimization and risk management.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Deep learning for exploring ultra-thin ferroelectrics with highly improved sensitivity of piezoresponse force microscopy

Hafnium oxide-based ferroelectrics have been extensively studied because of their existing ferroelectricity, even in ultra-thin film form. However, studying the weak response from ultra-thin film requires improved measurement sensitivity. In general, resonance-enhanced piezoresponse force microscopy (PFM) has been used to characterize ferroelectricity by fitting a simple harmonic oscillation model with the resonance spectrum. However, an iterative approach, such as traditional least squares (LS) fitting, is sensitive to noise and can result in the misunderstanding of weak responses. In this study, we developed the deep neural network (DNN) hybrid with deep denoising autoencoder (DDA) and principal component analysis (PCA) to extract resonance information. The DDA/PCA-DNN improves the PFM sensitivity down to 0.3 pm, allowing measurement of weak piezoresponse with low excitation voltage in 10-nm-thick Hf 0.5 Zr 0.5 O 2 thin films. Our hybrid approaches could provide more chances to explore the low piezoresponse of the ultra-thin ferroelectrics and could be applied to other microscopic techniques.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Probing the Hydrogen Enhanced Near-Field Emission of ITO without a Vacuum-Gap

In-situ monitoring of the multi-component gas streams in high temperature energy conversion devices offer the promises to higher efficiency via improved understanding of the chemical environments during device operation. While conventional resistive based metal oxide semiconductor gas sensors suffer from strong cross-sensitivity, optical sensing approaches offer intrinsic advantages to achieve gas selectivity based on wavelength specific interactions. This manuscript describes a novel method to achieve multicomponent gas sensing during gas exposure of H2, CO2, CH4and CO in humid high temperature environments. A single sensor element comprised of a perovskite La0.3Sr0.7TiO3(LSTO) oxide thin film layer coated on silica optical fiber was used. The sensing responses consisted of two wavelength-specific near infrared (NIR) mechanisms, namely broadband absorption associated with the metal oxide layer, and wavelength localized thermal emission responses associated with the hydroxyl defects within the silica fiber. Principal component analysis (PCA) was applied to couple the two mechanisms to achieve selective gas identification. Successful discrimination of H2and CO2on a single fiber sensor was achieved, where the results are both stable and reversible. This design demonstrates that by coupling multiple optical mechanisms on a single oxide coated fiber sensor, simple platforms can also achieve multi-component sensing functionality without the added complexity of a sensor array. Thus, it suggests a new approach to construct simple, robust and functional sensor designs capable of gas discrimination and quantification in multi-component gas streams.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel

Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Local coexistence of VO 2 phases revealed by deep data analysis

We report a synergistic approach of micro-Raman spectroscopic mapping and deep data analysis to study the distribution of crystallographic phases and ferroelastic domains in a defected Al-doped VO 2 microcrystal. Bayesian linear unmixing revealed an uneven distribution of the T phase, which is stabilized by the surface defects and uneven local doping that went undetectable by other classical analysis techniques such as PCA and SIMPLISMA. This work demonstrates the impact of information recovery via statistical analysis and full mapping in spectroscopic studies of vanadium dioxide systems, which is commonly substituted by averaging or single point-probing approaches, both of which suffer from information misinterpretation due to low resolving power.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Diffusion in biofilms respiring on electrodes

The goal of this study was to measure spatially and temporally resolved effective diffusion coefficients (De) in biofilms respiring on electrodes. Two model electrochemically active biofilms, Geobacter sulfurreducens PCA and Shewanella oneidensis MR-1, were investigated. A novel nuclear magnetic resonance microimaging perfusion probe capable of simultaneous electrochemical and pulsed-field gradient nuclear magnetic resonance (PFG-NMR) techniques was used. PFG-NMR allowed for noninvasive, nondestructive, high spatial resolution in situ De measurements in living biofilms respiring on electrodes. The electrodes were polarized so that they would act as the sole terminal electron acceptor for microbial metabolism. We present our results as both two-dimensional De heat maps and surface-averaged relative effective diffusion coefficient (Drs) depth profiles. We found that (1) Drs decreases with depth in G. sulfurreducens biofilms, following a sigmoid shape; (2) Drs at a given location decreases with G. sulfurreducens biofilm age; (3) average De and Drs profiles in G. sulfurreducens biofilms are lower than those in S. oneidensis biofilms—the G. sulfurreducens biofilms studied here were on average 10 times denser than the S. oneidensis biofilms; and (4) halting the respiration of a G. sulfurreducens biofilm decreases the De values. Density, reflected by De, plays a major role in the extracellular electron transfer strategies of electrochemically active biofilms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

In situ molecular imaging of hydrated biofilm in a microfluidic reactor by ToF-SIMS

The first results of using a novel single channel microfluidic reactor to enable Shewanella biofilm growth and in situ characterization using time-of-flight secondary ion mass spectrometry (ToF-SIMS) in the hydrated environment are presented. The new microfluidic interface allows direct probing of the liquid surface using ToF-SIMS, a vacuum surface technique. The detection window is an aperture of 2 m in diameter on a thin silicon nitride (SiN) membrane and it allows direct detection of the liquid surface. Surface tension of the liquid flowing inside the microchannel holds the liquid within the aperture. ToF-SIMS depth profiling was used to drill through the SiN membrane and the biofilm grown on the substrate. In situ 2D imaging of the biofilm in hydrated state was acquired, providing spatial distribution of the chemical compounds in the biofilm system. This data was compared with a medium filled microfluidic reactor devoid of biofilm and dried biofilm samples deposited on clean silicon wafers. Principle Component Analysis (PCA) was used to investigate these observations. Our results show that imaging biofilms in the hydrated environment using ToF-SIMS is possible using the unique microfluidic reactor. Moreover, characteristic biofilm fatty acids fragments were observed in the hydrated biofilm grown in the microfluidic channel, illustrating the advantage of imaging biofilm in its native environment.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2017

A comprehensive study on the structural evolution of HfO 2 thin films doped with various dopants

The origin of the unexpected ferroelectricity in doped HfO 2 thin films is now considered to be the formation of a non-centrosymmetric Pca 2 1 orthorhombic phase. Due to the polycrystalline nature of the films as well as their extremely small thickness (~10 nm) and mixed orientation and phase composition, structural analysis of doped HfO 2 thin films remains a challenging task. As a further complication, the structural similarities of the orthorhombic and tetragonal phase are difficult to distinguish by typical structural analysis techniques such as X-ray diffraction. To resolve this issue, the changes in the grazing incidence X-ray diffraction (GIXRD) patterns of HfO 2 films doped with Si, Al, and Gd are systematically examined. For all dopants, the shift of o 111/ t 101 diffraction peak is observed with increasing atomic layer deposition (ALD) cycle ratio, and this shift is thought to originate from the orthorhombic to P 4 2 / nmc tetragonal phase transition with decreasing aspect ratio (2 a /(b + c) for orthorhombic and c/a for the tetragonal phase). For quantitative phase analysis, Rietveld refinement is applied to the GIXRD patterns. A progressive phase transition from P 2 1 /c monoclinic to orthorhombic to tetragonal is confirmed for all dopants, and a strong relationship between orthorhombic phase fraction and remanent polarization value is uniquely demonstrated. The concentration range for the ferroelectric properties was the narrowest for the Si-doped HfO 2 films. As a result, the dopant size is believed to strongly affect the concentration range for the ferroelectric phase stabilization, since small dopants can strongly decrease the free energy of the tetragonal phase due to their shorter metal–oxygen bonds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗