The Tolman electronic parameter (TEP) derived from the A1-symmetrical CO stretching frequency of nickel-tricarbonyl complexes L-Ni(CO)3 with varying ligands L is misleading as (i) it is not based on a mode decoupled CO stretching frequency and (ii) a generally applicable and quantitatively correct or at least qualitatively reasonable relationship between the TEP and the metal-ligand bond strength does not exist. This is shown for a set of 181 nickel-tricarbonyl complexes using both experimental and calculated TEP values. Even the use of mode-mode decoupled CO stretching frequencies (L(ocal)TEPs) does not lead to a reliable description of the metal-ligand bond strength. This is obtained by introducing a new electronic parameter that is directly based on the metal-ligand local stretching force constant. For the test set of 181 nickel complexes, a direct metal-ligand electronic parameter (MLEP) in the form of a bond strength order is derived, which reveals that phosphines and related ligands (amines, arsines, stibines, bismuthines) are bonded to Ni both by σ-donation and π-back-donation. The strongest Ni-L bonds are identified for carbenes and cationic ligands. The new MLEP quantitatively assesses electronic and steric factors.
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http://dx.doi.org/10.1021/acs.inorgchem.5b02711 | DOI Listing |
Front Plant Sci
January 2025
Institute of Crop Science, Huzhou Academy of Agriculture Sciences, Huzhou, China.
With the rapid advancement of plant phenotyping research, understanding plant genetic information and growth trends has become crucial. Measuring seedling length is a key criterion for assessing seed viability, but traditional ruler-based methods are time-consuming and labor-intensive. To address these limitations, we propose an efficient deep learning approach to enhance plant seedling phenotyping analysis.
View Article and Find Full Text PDFPhys Chem Chem Phys
January 2025
School of Science, Constructor University, Bremen 28359, Germany.
We present a machine learning (ML) workflow for optimizing electronic band structures using density functional tight binding (DFTB) to replicate the results of costly hybrid functional calculations. The workflow is trained on carbon, silicon, and silicon carbide systems, encompassing bulk, slab, and defect geometries. Our method accurately reproduces hybrid functional results by applying a DFTB-ML scheme to train and predict the scaling parameters of two-center integrals and on-site energies, which is particularly accurate for electronic band structures near the Fermi energy.
View Article and Find Full Text PDFBMC Pregnancy Childbirth
January 2025
Reproductive Center of Shenzhen Zhongshan Obstetrics and Gynecology Hospital Formerly Reproductive Center of Shenzhen Zhongshan Urology Hospital, Shenzhen, Guangdong Province, China.
Objective: To develop a predictive tool in the form of a Nomogram based on the Cox regression model, which incorporates the impact of the length of treatment cycles on the outcome of live birth, to evaluate the probability of infertile couples having a live birth after one or more complete cycles of In Vitro Fertilization (IVF), and to provide patients with a risk assessment that is easy to understand and visualize.
Methods: A retrospective study for establishing a prediction model was conducted in the reproductive center of Shenzhen Zhongshan Obstetrics & Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital). A total of 4413 patients who completed ovarian stimulation treatment and reached the trigger were involved.
Sci Rep
January 2025
Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233, Gdansk, Poland.
Computational tools, particularly electromagnetic (EM) solvers, are now commonplace in antenna design. While ensuring reliability, EM simulations are time-consuming, leading to high costs associated with EM-driven procedures like parametric optimization or statistical design. Various techniques have been developed to address this issue, with surrogate modeling methods garnering particular attention due to their potential advantages.
View Article and Find Full Text PDFSci Rep
January 2025
Electronics and Communication Engineering Department, Mansoura University, Mansoura, 35516, Egypt.
As the world recovered from the coronavirus, the emergence of the monkeypox virus signaled a potential new pandemic, highlighting the need for faster and more efficient diagnostic methods. This study introduces a hybrid architecture for automatic monkeypox diagnosis by leveraging a modified grey wolf optimization model for effective feature selection and weighting. Additionally, the system uses an ensemble of classifiers, incorporating confusion based voting scheme to combine salient data features.
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