Optically detected magnetic resonance spectroscopic analyses on the role of magnetic ions in colloidal nanocrystals

Incorporating magnetic ions into semiconductor nanocrystals has emerged as a prominent research field for manipulating spin-related properties. The magnetic ions within the host semiconductor experience spin-exchange interactions with photogenerated carriers and are often involved in the recombination routes, stimulating special magneto-optical effects. The current account presents a comparative study, emphasizing the impact of engineering nanostructures and selecting magnetic ions in shaping carrier-magnetic ion interactions. Various host materials, including the II-VI group, halide perovskites, and I-III-VI2 in diverse structural configurations such as core/shell quantum dots, seeded nanorods, and nanoplatelets, incorporated with magnetic ions such as Mn2+, Ni2+, and Cu1+/2+ are highlighted. These materials have recently been investigated by us using state-of-the-art steady-state and transient optically detected magnetic resonance (ODMR) spectroscopy to explore individual spin-dynamics between the photogenerated carriers and magnetic ions and their dependence on morphology, location, crystal composition, and type of the magnetic ion. The information extracted from the analyses of the ODMR spectra in those studies exposes fundamental physical parameters, such as g-factors, exchange coupling constants, and hyperfine interactions, together providing insights into the nature of the carrier (electron, hole, dopant), its local surroundings (isotropic/anisotropic), and spin dynamics. The findings illuminate the importance of ODMR spectroscopy in advancing our understanding of the role of magnetic ions in semiconductor nanocrystals and offer valuable knowledge for designing magnetic materials intended for various spin-related technologies

Resonances On-Demand for Plasmonic Nano-Particles

A method for designing plasmonic particles with desired resonance spectra is presented. The method is based on repetitive perturbations of an initial particle shape while calculating the eigenvalues of the various quasistatic resonances. The method is rigorously proved, assuring a solution exists for any required spectral resonance location. Resonances spanning the visible and the near-infrared regimes, as designed by our method, are verified using finite-difference time-domain simulations. A novel family of particles with collocated dipole-quadrupole resonances is designed, demonstrating the unique power of the method. Such on-demand engineering enables strict realization of nano-antennas and metamaterials for various applications requiring specific spectral functions

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Ironic and Overcompensating Processes Under Avoidance Instructions in Motor Tasks

Thought-suppression research showed, when asked to suppress a given thought (e.g., a white bear), people ironically report thinking more of the suppressed thought. Testing motor performance given avoidance goals (e.g., avoid putting the ball short of the target in golf) represents an interest to transfer thought-suppression findings to motor tasks. However, instead of revealing an ironic process, motor studies showed mixed results, suggesting a coexistence of ironic and overcompensating processes. The present study investigates the coexistence of ironic and overcompensating processes induced by avoidance goals in motor tasks. Adopting a dual-process framework, an Attention Imbalance Model (AIM) was proposed to conceptualize such a coexistence. Four golf-putting experiments were conducted to test the AIM by manipulating the degree of attentional imbalance. Results indicated the factor of attentional imbalance moderates the likelihood between ironic and overcompensating processes in golf putting, and such a moderating effect demands task-specific considerations. In addition, performance feedback confounded the putting performance by reducing the likelihood of overcompensating process. The implications of the AIM are discussed in an extended context of motor performance under avoidance goals and thought suppression

An artificial intelligence algorithm for automated blastocyst morphometric parameters demonstrates a positive association with implantation potential

Abstract Blastocyst selection is primarily based on morphological scoring systems and morphokinetic data. These methods involve subjective grading and time-consuming techniques. Artificial intelligence allows for objective and quick blastocyst selection. In this study, 608 blastocysts were selected for transfer using morphokinetics and Gardner criteria. Retrospectively, morphometric parameters of blastocyst size, inner cell mass (ICM) size, ICM-to-blastocyst size ratio, and ICM shape were automatically measured by a semantic segmentation neural network model. The model was trained on 1506 videos with 102 videos for validation with no overlap between the ICM and trophectoderm models. Univariable logistic analysis found blastocyst size and ICM-to-blastocyst size ratio to be significantly associated with implantation potential. Multivariable regression analysis, adjusted for woman age, found blastocyst size to be significantly associated with implantation potential. The odds of implantation increased by 1.74 for embryos with a blastocyst size greater than the mean (147 ± 19.1 μm). The performance of the algorithm was represented by an area under the curve of 0.70 (p < 0.01). In conclusion, this study supports the association of a large blastocyst size with higher implantation potential and suggests that automatically measured blastocyst morphometrics can be used as a precise, consistent, and time-saving tool for improving blastocyst selection