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Lead‐based relaxor ferroelectrics are characterized by outstanding piezoelectric and dielectric properties, making them useful in a wide range of applications. Despite the numerous models proposed to describe the relation between their nanoscale polar structure and the large properties, the multiple contributions to these properties are not yet revealed. Here, by combining atomistic and mesoscopic‐scale structural analyses with macroscopic piezoelectric and dielectric measurements across the (100–x)Pb(Mg_1/3Nb_2/3)O₃–xPbTiO₃(PMN–xPT) phase diagram, a direct link is established between the multiscale structure and the large nonlinear macroscopic response observed in the monoclinic PMN‐xPT compositions. The approach reveals a previously unrecognized softening effect, which is common to Pb‐based relaxor ferroelectrics and arises from the displacements of low‐angle nanodomain walls, facilitated by the nanoscale polar character and lattice strain disorder. This comprehensive comparative study points to the multiple, distinct mechanisms that are responsible for the large piezoelectric response in relaxor ferroelectrics.

 

The number of applications of informatics or data‐driven discovery is growing in many fields, including materials science. The large amount of data that is readily available, combined with high‐level statistical algorithms, is proving to be extremely useful in developing complex predictive models with little to no human supervision or bias. However, in the field of soft matter, which includes complex materials such as polymers, liquids, emulsions, colloids, and gels, there is a slower adoption of informatics strategies than in adjacent fields. Here, the current state of soft matter informatics is discussed. Challenges specific to soft materials, including data classification, various degrees of organization at multiple length scales, and process‐dependent properties require unique approaches by researchers in order to develop robust informatics approaches in soft matter. The current ability to extract and analyze the information from the PoLyInfo database is demonstrated by the fitting of the Flory–Fox equation for glass transition temperature for several polymers. This Progress Report serves to introduce and excite the scientific community about the remarkable potential of informatics for exploring the properties of soft materials.

 

Data‐driven science and technology have helped achieve meaningful technological advancements in areas such as materials/drug discovery and health care, but efforts to apply high‐end data science algorithms to the areas of glass and ceramics are still limited. Many glass and ceramic researchers are interested in enhancing their work by using more data and data analytics to develop better functional materials more efficiently. Simultaneously, the data science community is looking for a way to access materials data resources to test and validate their advanced computational learning algorithms. To address this issue, The American Ceramic Society (ACerS) convened a Glass and Ceramic Data Science Workshop in February 2018, sponsored by the National Institute for Standards and Technology (NIST) Advanced Manufacturing Technologies (AMTech) program. The workshop brought together a select group of leaders in the data science, informatics, and glass and ceramics communities, ACerS, and Nexight Group to identify the greatest opportunities and mechanisms for facilitating increased collaboration and coordination between these communities. This article summarizes workshop discussions about the current challenges that limit interactions and collaboration between the glass and ceramic and data science communities, opportunities for a coordinated approach that leverages existing knowledge in both communities, and a clear path toward the enhanced use of data science technologies for functional glass and ceramic research and development.

 

Organic solar cells (OSCs) are one of the most promising cost‐effective options for utilizing solar energy, and, while the field of OSCs has progressed rapidly in device performance in the past few years, the stability of nonfullerene OSCs has received less attention. Developing devices with both high performance and long‐term stability remains challenging, particularly if the material choice is restricted by roll‐to‐roll and benign solvent processing requirements and desirable mechanical durability. Building upon the ink (toluene:FTAZ:IT‐M) that broke the 10% benchmark when blade‐coated in air, a second donor material (PBDB‐T) is introduced to stabilize and enhance performance with power conversion efficiency over 13% while keeping toluene as the solvent. More importantly, the ternary OSCs exhibit excellent thermal stability and storage stability while retaining high ductility. The excellent performance and stability are mainly attributed to the inhibition of the crystallization of nonfullerene small‐molecular acceptors (SMAs) by introducing a stiff donor that also shows low miscibility with the nonfullerene SMA and a slightly higher highest occupied molecular orbital (HOMO) than the host polymer. The study indicates that improved stability and performance can be achieved in a synergistic way without significant embrittlement, which will accelerate the future development and application of nonfullerene OSCs.

 

Intra‐ and intermolecular ordering greatly impacts the electronic and optoelectronic properties of semiconducting polymers. The interrelationship between ordering of alkyl sidechains and conjugated backbones has yet to be fully detailed, despite much prior effort. Here, the discovery of a highly ordered alkyl sidechain phase in six representative semiconducting polymers, determined from distinct spectroscopic and diffraction signatures, is reported. The sidechain ordering exhibits unusually large coherence lengths (≥70 nm), induces torsional/twisting backbone disorder, and results in a vertically multilayered nanostructure with ordered sidechain layers alternating with disordered backbone layers. Calorimetry and in situ variable temperature scattering measurements in a model system poly{4‐(5‐(4,8‐bis(3‐butylnonyl)‐6‐methylbenzo[1,2‐b:4,5‐b′]dithiophen‐2‐yl)thiophen‐2‐yl)‐2‐(2‐butyloctyl)‐5,6‐difluoro‐7‐(5‐methylthiophen‐2‐yl)‐2H‐benzo[d][1,2,3]triazole} (PBnDT‐FTAZ) clearly delineate this competition of ordering that prevents simultaneous long‐range order of both moieties. The long‐range sidechain ordering can be exploited as a transient state to fabricate PBnDT‐FTAZ films with an atypical edge‐on texture and 2.5× improved field‐effect transistor mobility. The observed influence of ordering between the moieties implies that improved molecular design can produce synergistic rather than destructive ordering effects. Given the large sidechain coherence lengths observed, such synergistic ordering should greatly improve the coherence length of backbone ordering and thereby improve electronic and optoelectronic properties such as charge transport and exciton diffusion lengths.

 

Diffraction analysis in four-dimensional scanning transmission electron microscopy now enables the mapping of local structures including symmetry, strain, and polarization of materials. However, measuring the distribution of these configurations at the unit cell level remains a challenge because most analysis methods require the diffraction disks to be separated, limiting the electron probe sizes to be larger than a unit cell. Here, we show improved spatial resolution in mapping the polarization displacement and phases of BaTiO₃sampled at a rate equivalent to the size of the projected unit cells using 4D-STEM. This improvement in spatial resolution is accomplished by masking out the overlapping regions in partially overlapped convergent beam electron diffraction patterns. By reducing the probe size to the order of single projected unit cells in size, the measurement shows local fluctuation within the nanosized rhombohedral domains in tetragonal phased BaTiO₃, indicating the origin of phase transition and evolution across different length scales.

 

Scientists use imaging to identify objects of interest and infer properties of these objects. The locations of these objects are often measured with error, which when ignored leads to biased parameter estimates and inflated variance. Current measurement error methods require an estimate or knowledge of the measurement error variance to correct these estimates, which may not be available. Instead, we create a spatial Bayesian hierarchical model that treats the locations as parameters, using the image itself to incorporate positional uncertainty. We lower the computational burden by approximating the likelihood using a noncontiguous block design around the object locations. We use this model to quantify the relationship between the intensity and displacement of hundreds of atom columns in crystal structures directly imaged via scanning transmission electron microscopy (STEM). Atomic displacements are related to important phenomena such as piezoelectricity, a property useful for engineering applications like ultrasound. Quantifying the sign and magnitude of this relationship will help materials scientists more precisely design materials with improved piezoelectricity. A simulation study confirms our method corrects bias in the estimate of the parameter of interest and drastically improves coverage in high noise scenarios compared to non-measurement error models.