Comment on “Using NMR to Test Molecular Mobility during a Chemical Reaction” ()

A study reported inThe Journal of Physical Chemistry Letters(Wang et al.,2021,12, 2370) of “boosted mobility” measured by diffusion NMR experiments contains significant errors in data analysis and interpretation. We carefully reanalyzed the same data and find no evidence of boosted mobility, and we identify several sources of error

Comment on "Boosted molecular mobility during common chemical reactions"

The apparent "boosted mobility"observed by Wang et al. (Reports, 31 July 2020, p. 537) is the result of a known artifact. When signal intensities are changing during a nuclear magnetic resonance (NMR) diffusion measurement for reasons other than diffusion, the use of monotonically increasing gradient amplitudes produces erroneous diffusion coefficients. We show that no boosted molecular mobility is observed when shuffled gradient amplitudes are applied

Following Molecular Mobility during Chemical Reactions: No Evidence for Active Propulsion

The reported changes in self-diffusion of small molecules during reactions have been attributed to "boosted mobility". We demonstrate the critical role of changing concentrations of paramagnetic ions on nuclear magnetic resonance (NMR) signal intensities, which led to erroneous measurements of diffusion coefficients. We present simple methods to overcome this problem. The use of shuffled gradient amplitudes allows accurate diffusion NMR measurements, even with time-dependent relaxation rates caused by changing concentrations of paramagnetic ions. The addition of a paramagnetic relaxation agent allows accurate determination of both diffusion coefficients and reaction kinetics during a single experiment. We analyze a copper-catalyzed azide-alkyne cycloaddition "click"reaction, for which boosted mobility has been claimed. With our methods, we accurately measure the diffusive behavior of the solvent, starting materials, and product and find no global increase in diffusion coefficients during the reaction. We overcome NMR signal overlap using an alternative reducing agent to improve the accuracy of the diffusion measurements. The alkyne reactant diffuses slower as the reaction proceeds due to binding to the copper catalyst during the catalytic cycle. The formation of this intermediate was confirmed by complementary NMR techniques and density functional theory calculations. Our work calls into question recent claims that molecules actively propel or swim during reactions and establishes that time-resolved diffusion NMR measurements can provide valuable insight into reaction mechanisms

Response to Comment on "following Molecular Mobility during Chemical Reactions: No Evidence for Active Propulsion" and "molecular Diffusivity of Click Reaction Components: The Diffusion Enhancement Question"

In their Comment (DOI: 10.1021/jacs.2c02965) on two related publications by our groups (J. Am. Chem. Soc. 2021, 143, 20884-20890; DOI: 10.1021/jacs.1c09455) and another (J. Am. Chem. Soc. 2022, 144, 1380-1388; DOI: 10.1021/jacs.1c11754), Huang and Granick discuss the diffusion NMR measurements of molecules during a copper-catalyzed azide-alkyne cycloaddition (CuAAC) "click"reaction. Here we respond to these comments and maintain that no diffusion enhancement was observed for any species during the reaction. We show that the relaxation agent does not interfere with the CuAAC reaction kinetics nor the diffusion of the molecules involved. Similarly, the gradient pulse length and diffusion time do not affect the diffusion coefficients. Peak overlap was completely removed in our study with the use of hydrazine as the reducing agent. The steady-state assumption does not hold for these diffusion measurements that take several minutes, which is the reason monotonic gradient orders are not suitable. Finally, we discuss the other reactions where similar changes in diffusion have been claimed. Our conclusions are fully supported by the results represented in our original JACS Article and the corresponding Supporting Information

Dusty Planetary Systems

Extensive photometric stellar surveys show that many main sequence stars show emission at infrared and longer wavelengths that is in excess of the stellar photosphere; this emission is thought to arise from circumstellar dust. The presence of dust disks is confirmed by spatially resolved imaging at infrared to millimeter wavelengths (tracing the dust thermal emission), and at optical to near infrared wavelengths (tracing the dust scattered light). Because the expected lifetime of these dust particles is much shorter than the age of the stars (>10 Myr), it is inferred that this solid material not primordial, i.e. the remaining from the placental cloud of gas and dust where the star was born, but instead is replenished by dust-producing planetesimals. These planetesimals are analogous to the asteroids, comets and Kuiper Belt objects (KBOs) in our Solar system that produce the interplanetary dust that gives rise to the zodiacal light (tracing the inner component of the Solar system debris disk). The presence of these "debris disks" around stars with a wide range of masses, luminosities, and metallicities, with and without binary companions, is evidence that planetesimal formation is a robust process that can take place under a wide range of conditions. This chapter is divided in two parts. Part I discusses how the study of the Solar system debris disk and the study of debris disks around other stars can help us learn about the formation, evolution and diversity of planetary systems by shedding light on the frequency and timing of planetesimal formation, the location and physical properties of the planetesimals, the presence of long-period planets, and the dynamical and collisional evolution of the system. Part II reviews the physical processes that affect dust particles in the gas-free environment of a debris disk and their effect on the dust particle size and spatial distribution.Comment: 68 pages, 25 figures. To be published in "Solar and Planetary Systems" (P. Kalas and L. French, Eds.), Volume 3 of the series "Planets, Stars and Stellar Systems" (T.D. Oswalt, Editor-in-chief), Springer 201

The antisaccade task as an index of sustained goal activation in working memory: modulation by nicotine

The antisaccade task provides a laboratory analogue of situations in which execution of the correct behavioural response requires the suppression of a more prepotent or habitual response. Errors (failures to inhibit a reflexive prosaccade towards a sudden onset target) are significantly increased in patients with damage to the dorsolateral prefrontal cortex and patients with schizophrenia. Recent models of antisaccade performance suggest that errors are more likely to occur when the intention to initiate an antisaccade is insufficiently activated within working memory. Nicotine has been shown to enhance specific working memory processes in healthy adults. MATERIALS AND METHODS: We explored the effect of nicotine on antisaccade performance in a large sample (N = 44) of young adult smokers. Minimally abstinent participants attended two test sessions and were asked to smoke one of their own cigarettes between baseline and retest during one session only. RESULTS AND CONCLUSION: Nicotine reduced antisaccade errors and correct antisaccade latencies if delivered before optimum performance levels are achieved, suggesting that nicotine supports the activation of intentions in working memory during task performance. The implications of this research for current theoretical accounts of antisaccade performance, and for interpreting the increased rate of antisaccade errors found in some psychiatric patient groups are discussed

Fault Trees from Data: Efficient Learning with an Evolutionary Algorithm

Cyber-physical systems come with increasingly complex architectures and failure modes, which complicates the task of obtaining accurate system reliability models. At the same time, with the emergence of the (industrial) Internet-of-Things, systems are more and more often being monitored via advanced sensor systems. These sensors produce large amounts of data about the components' failure behaviour, and can, therefore, be fruitfully exploited to learn reliability models automatically. This paper presents an effective algorithm for learning a prominent class of reliability models, namely fault trees, from observational data. Our algorithm is evolutionary in nature; i.e., is an iterative, population-based, randomized search method among fault-tree structures that are increasingly more consistent with the observational data. We have evaluated our method on a large number of case studies, both on synthetic data, and industrial data. Our experiments show that our algorithm outperforms other methods and provides near-optimal results.Comment: This paper is an extended version of the SETTA 2019 paper, Springer-Verla