Navigating Ball Mill Specifications for Theory-to-Practice Reproducibility in Mechanochemistry.

The rising prospects of mechanochemically assisted syntheses hold promise for both academia and industry, yet they face challenges in understanding and, therefore, anticipating respective reaction kinetics. Particularly, dependencies based on variations in milling equipment remain little understood and globally overlooked. This study aims to address this issue by identifying critical parameters through kinematic models, facilitating the reproducibility of mechanochemical reactions across the most prominent mills in laboratory settings, namely planetary and mixer mills. Through a series of selected experiments replicating major classes of organic, organometallic, transition metal-catalyzed, and inorganic reactions from literature, we rationalize the independence of kinematic parameters on reaction kinetics when the accumulated energy criterion is met. As a step forward and to facilitate the practicability of our findings, we provide a freely accessible online tool† that allows the calculation of respective energy parameters for different planetary and mixer mills. Our work advances the current understanding of mechanochemistry and lays the foundation for future rational exploration in this rapidly evolving field.

Multi-Ethnic Norms for Volumes of Subcortical and Lobar Brain Structures Measured by Neuro I: Ethnicity May Improve the Diagnosis of Alzheimer's Disease1.

Background: We previously demonstrated the validity of a regression model that included ethnicity as a novel predictor for predicting normative brain volumes in old age. The model was optimized using brain volumes measured with a standard tool FreeSurfer. Objective: Here we further verified the prediction model using newly estimated brain volumes from Neuro I, a quantitative brain analysis system developed for Korean populations. Methods: Lobar and subcortical volumes were estimated from MRI images of 1,629 normal Korean and 786 Caucasian subjects (age range 59-89) and were predicted in linear regression from ethnicity, age, sex, intracranial volume, magnetic field strength, and scanner manufacturers. Results: In the regression model predicting the new volumes, ethnicity was again a substantial predictor in most regions. Additionally, the model-based z-scores of regions were calculated for 428 AD patients and the matched controls, and then employed for diagnostic classification. When the AD classifier adopted the z-scores adjusted for ethnicity, the diagnostic accuracy has noticeably improved (AUC = 0.85, ΔAUC = + 0.04, D = 4.10, p < 0.001). Conclusions: Our results suggest that the prediction model remains robust across different measurement tool, and ethnicity significantly contributes to the establishment of norms for brain volumes and the development of a diagnostic system for neurodegenerative diseases.

Electron beam-induced demetallation of Fe, Co, Ni, Cu, Zn, Pd, and Pt metalloporphyrins: insights in e-beam chemistry and metal cluster formations.

Electron beams are versatile tools for nanoscale fabrication processes, however, the underlying e-beam chemistry remains in its infancy. Through operando transmission electron microscopy investigations, we elucidate a redox-driven cargo release of individual metal atoms triggered by electron beams. The chosen organic delivery molecule, tetraphenylporphyrin (TPP), proves highly versatile, forming complexes with nearly all metals from the periodic table and being easily processed in solution. A comprehensive cinematographic analysis of the dynamics of single metal atoms confirms the nearly instantaneous ejection of complexed metal atoms under an 80 kV electron beam, underscoring the system's broad versatility. Providing mechanistic insights, we employ density functional theory to support the proposed reductive demetallation pathway facilitated by secondary electrons, contributing novel perspectives to electron beam-mediated chemical reaction mechanisms. Lastly, our findings demonstrate that all seven metals investigated form nanoclusters once ejected from TPP, highlighting the method's potential for studying and developing sustainable single-atom and nanocluster catalysts.

A magnetically powered nanomachine with a DNA clutch.

Machines found in nature and human-made machines share common components, such as an engine, and an output element, such as a rotor, linked by a clutch. This clutch, as seen in biological structures such as dynein, myosin or bacterial flagellar motors, allows for temporary disengagement of the moving parts from the running engine. However, such sophistication is still challenging to achieve in artificial nanomachines. Here we present a spherical rotary nanomotor with a reversible clutch system based on precise molecular recognition of built-in DNA strands. The clutch couples and decouples the engine from the machine's rotor in response to encoded inputs such as DNA or RNA. The nanomotor comprises a porous nanocage as a spherical rotor to confine the magnetic engine particle within the nanospace (∼0.004 µm3) of the cage. Thus, the entropically driven irreversible disintegration of the magnetic engine and the spherical rotor during the disengagement process is eliminated, and an exchange of microenvironmental inputs is possible through the nanopores. Our motor is only 200 nm in size and the clutch-mediated force transmission powered by an embedded ferromagnetic nanocrystal is high enough (∼15.5 pN at 50 mT) for the in vitro mechanical activation of Notch and integrin receptors, demonstrating its potential as nano-bio machinery.