arXiv subjects

Chunbo Lin

Publications and source records attributed to Chunbo Lin.

3 recordsLinked to original sources

arXiv2026

Direct experimental measurement of femtonewton-scale momentum transfer force from electron beams

Electron beams (e-beams) are ubiquitous in imaging, patterning, and propulsion. This prevalence is rooted in the profound mastery of their wave-particle duality and energy-transfer pathways. Yet, a fundamental dimension remains largely unexplored: while the mechanical effect (i.e., the momentum transfer to a target) is theoretically known, quantification of its femtonewton-range force has remained elusive. This discrepancy represents a missing piece of the puzzle toward a comprehensive understanding of e-beam-matter interactions, and ultimately limits the multi-dimensional exploitation of e-beams. A force sensor combining femtonewton sensitivity, immunity to electromagnetic noise, compatibility with vacuum, and absolute calibration is critical to bridge the gap between theory and experiment. Here the FINEST (Femtonewton Interferometric Nanomechanical Electron-beam Sensing Technology) sensor is proposed and successfully tested to measure the force of an e-beam. FINEST is an optical-pressure-calibrated 3D spring-type optical sensor that operates reliably under e-beam conditions. Femtonewton-scale forces from 2-30 keV e-beams are directly measured, ranging from 505 fN to 13 pN. Both linear scaling with beam current and a non-monotonic energy dependence (peaking near 10 keV) are observed. Based on this calibrated force, the mechanical contribution to e-beam ice etching was quantitatively confirmed; its effect is orders of magnitude lower than the total etch depth and lacks noticeable energy dependence. By achieving the first direct experimental measurement of e-beam momentum transfer, this work adds a long-missing dimension to the physical landscape of e-beam processes. These findings provide a quantitative basis for furthering the multi-dimensional exploitation of e-beams, potentially transforming our approach to precision nanofabrication, sensing, and fundamental electron physics research.

physics.optics↗

arXiv2024

Constraining the modified friction in gravitational wave propagation with precessing black hole binaries

A broad class of modified gravities can result in a modified friction effect in the propagation of gravitational waves (GWs). This effect changes the amplitude-damping rate of GWs during their propagation in the cosmological distance and thus modifies the standard luminosity distance of GWs in general relativity. Therefore, one can constrain this modified friction by measuring both the luminosity distance and redshift of the GW sources. In this paper, we investigate the prospects of constraining such modified friction effects by using the precessing binary black holes with ground-based GW detectors. For this purpose, we consider 20 precessing events detected by the GW detector network consisting of two LIGO detectors and two third-generation GW detectors (the Einstein Telescope and the Cosmic Explorer). The redshift information of these events is obtained by identifying their possible host galaxies in the GLADE+ galaxy catalog. We show that the precession in the binary system can significantly improve the precision of the luminosity distance and thus lead to a tighter constraint on the modified friction. By assuming narrow priors on cosmological parameters that are consistent with the uncertainties of Planck 2018 results, our analysis shows that the modified friction effect, characterized by two parameters $(\Xi_0, n)$, can be constrained to be $\Xi_0 = 1.002^{+0.004}_{-0.004}$ and $n=3.257^{+2.595}_{-2.192}$, in which the result of $\Xi_0$ is about 2 orders of magnitude better than current results from an analysis with GWTC-3. Our result sets the stage for future research with third-generation GW detectors, offering new insights into gravitational parameter modifications. It also contributes to the understanding of the properties and applications of binary black hole systems with precession.

gr-qc↗

arXiv2024

Interpretable Machine Learning Strategies for Accurate Prediction of Thermal Conductivity in Polymeric Systems

Polymers, integral to advancements in high-tech fields, necessitate the study of their thermal conductivity (TC) to enhance material attributes and energy efficiency. The TC of polymers obtained by molecular dynamics (MD) calculations and experimental measurements is slow, and it is difficult to screen polymers with specific TC in a wide range. Existing machine learning (ML) techniques for determining polymer TC suffer from the problems of too large feature space and cannot guarantee very high accuracy. In this work, we leverage TCs from accessible datasets to decode the Simplified Molecular Input Line Entry System (SMILES) of polymers into ten features of distinct physical significance. A novel evaluation model for polymer TC is formulated, employing four ML strategies. The Gradient Boosting Decision Tree (GBDT)-based model, a focal point of our design, achieved a prediction accuracy of R$^2$=0.88 on a dataset containing 400 polymers. Furthermore, we used an interpretable ML approach to discover the significant contribution of quantitative estimate of drug-likeness and number of rotatable bonds features to TC, and analyzed the physical mechanisms involved. The ML method we developed provides a new idea for physical modeling of polymers, which is expected to be generalized and applied widely in constructing polymers with specific TCs and predicting all other properties of polymers.

physics.app-ph↗