arXiv subjects

Christian Pfeiffer

Publications and source records attributed to Christian Pfeiffer.

8 recordsLinked to original sources

arXiv2026

Chiral-Induced Spin Selectivity Effect in a 1 nm Thin 1,1'-Binaphthyl-2,2'-diyl Hydrogenphosphate Self-Assembled Monolayer on Nickel Oxide

The chiral-induced spin selectivity (CISS) effect describes an observed correlation between the orientation of an electron spin transported or transferred through a molecule and that molecule's chirality. Suitable molecules are usually arranged as self-assembled monolayers (SAMs), and the primary CISS systems are based on multiple nanometer-long biomolecules exhibiting helical chirality. Aside from these typically thiolate-anchored molecules, phosphonic and phosphoric acid SAMs may well become significant for those CISS applications that require a more robust molecular coupling to metal oxide surfaces. In this work, we report on our studies, employing the aromatic, low-molecular-mass, axially chiral organophosphoric acid derivative 1,1'-binaphthyl-2,2'-diyl hydrogenphosphate (BNP). Grown as a roughly 1 nm thin SAM on top of a NiOx/Ni substrate, a strong circular dichroism signal indicates that the thin films preserved chirality. The CISS response exhibits a high magnetoresistance with a spin polarization of 50-80% when measured using magnetic-conductive atomic force microscopy. For biases above 0.5 V, the magnetoresistance curves could be well fitted to the Fowler-Nordheim (FN) tunneling model. Using a minimal FN model, we determined that, depending on the magnetization direction and the handedness of the molecules, electrons of a certain spin direction face an effective tunneling barrier at high bias, which is either 80 % higher or 40 % lower compared to the barrier for electrons of the opposite spin direction. Due to the small size of the molecules, their compatibility with oxide materials, and their commercial availability, they are excellent candidates for the realization of novel (nanoscale) organic spintronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Mono-exponential Current Attenuation with Distance across 16 nm Thick Bacteriorhodopsin Multilayers

The remarkable ability of natural proteins to conduct electricity in the dry state over long distances remains largely inexplicable despite intensive research. In some cases, a (weakly) exponential length-attenuation, as in off-resonant tunneling transport, extends to thicknesses even beyond 10 nm. This report deals with such charge transport characteristics observed in self-assembled multilayers of the protein bacteriorhodopsin (bR). About 7.5 nm to 15.5 nm thick bR layers were prepared on conductive titanium nitride (TiN) substrates using aminohexylphosphonic acid and poly-diallyl-dimethylammonium electrostatic linkers. Using conical EGaIn top contacts, an intriguing, mono-exponential conductance attenuation as a function of the bR layer thickness with a small attenuation coefficient $\beta \approx 0.8 \space {\rm nm}^{-1}$ is measured at zero bias. Variable-temperature measurements using evaporated Ti/Au top contacts yield effective energy barriers of about 100 meV from fitting the data to tunneling, hopping, and carrier cascade transport models. The observed temperature-dependence is assigned to the protein-electrode interfaces. The transport length and temperature dependence of the current densities are consistent with tunneling through the protein-protein and protein-electrode interfaces, respectively. Importantly, our results call for new theoretical approaches to find the microscopic mechanism behind the remarkably efficient, long-range electron transport within bR.

physics.bio-ph↗

arXiv2023

A Tight Formulation for the Dial-a-Ride Problem

Ridepooling services play an increasingly important role in modern transportation systems. With soaring demand and growing fleet sizes, the underlying route planning problems become increasingly challenging. In this context, we consider the dial-a-ride problem (DARP): Given a set of transportation requests with pick-up and delivery locations, passenger numbers, time windows, and maximum ride times, an optimal routing for a fleet of vehicles, including an optimized passenger assignment, needs to be determined. We present tight mixed-integer linear programming (MILP) formulations for the DARP by combining two state-of-the-art models into novel location-augmented-event-based formulations. Strong valid inequalities and lower and upper bounding techniques are derived to further improve the formulations. We then demonstrate the theoretical and computational superiority of the new model: First, the formulation is tight in the sense that, if time windows shrink to a single point in time, the linear programming relaxation yields integer (and hence optimal) solutions. Second, extensive numerical experiments on benchmark instances show that computational times are on average reduced by 49.7% compared to state-of-the-art event-based approaches.

math.OC↗

arXiv2023

Microgravity induces overconfidence in perceptual decision-making

Does gravity affect decision-making? This question comes into sharp focus as plans for interplanetary human space missions solidify. In the framework of Bayesian brain theories, gravity encapsulates a strong prior, anchoring agents to a reference frame via the vestibular system, informing their decisions and possibly their integration of uncertainty. What happens when such a strong prior is altered? We address this question using a self-motion estimation task in a space analog environment under conditions of altered gravity. Two participants were cast as remote drone operators orbiting Mars in a virtual reality environment on board a parabolic flight, where both hyper- and microgravity conditions were induced. From a first-person perspective, participants viewed a drone exiting a cave and had to first predict a collision and then provide a confidence estimate of their response. We evoked uncertainty in the task by manipulating the motion's trajectory angle. Post-decision subjective confidence reports were negatively predicted by stimulus uncertainty, as expected. Uncertainty alone did not impact overt behavioral responses (performance, choice) differentially across gravity conditions. However microgravity predicted higher subjective confidence, especially in interaction with stimulus uncertainty. These results suggest that variables relating to uncertainty affect decision-making distinctly in microgravity, highlighting the possible need for automatized, compensatory mechanisms when considering human factors in space research.

cs.RO↗

arXiv2022

A Branch-and-Cut algorithm for an urban dial-a-ride problem

The paper investigates a dial-a-ride problem focusing on the residents of large cities. These individuals have the opportunity to use a wide variety of transportation modes. Because of this, ridepooling providers have to solve the tradeoff between a high pooling rate and a small detour for customers to be competitive. We provide a Branch-and-Cut algorithm for this problem setting and introduce a new technique using information about already fixed paths to identify infeasible solutions ahead of time and to improve lower bounds on the arrival times at customer locations. By this, we are able to introduce additional valid inequalities to improve the search. We evaluate our procedure in an extensive computational study with up to 120 customers and ten vehicles. Our procedure finds significantly more optimal solutions and better lower and upper bounds in comparison with a mixed-integer programming formulation.

math.OC↗

arXiv2022

Visual Attention Prediction Improves Performance of Autonomous Drone Racing Agents

Humans race drones faster than neural networks trained for end-to-end autonomous flight. This may be related to the ability of human pilots to select task-relevant visual information effectively. This work investigates whether neural networks capable of imitating human eye gaze behavior and attention can improve neural network performance for the challenging task of vision-based autonomous drone racing. We hypothesize that gaze-based attention prediction can be an efficient mechanism for visual information selection and decision making in a simulator-based drone racing task. We test this hypothesis using eye gaze and flight trajectory data from 18 human drone pilots to train a visual attention prediction model. We then use this visual attention prediction model to train an end-to-end controller for vision-based autonomous drone racing using imitation learning. We compare the drone racing performance of the attention-prediction controller to those using raw image inputs and image-based abstractions (i.e., feature tracks). Comparing success rates for completing a challenging race track by autonomous flight, our results show that the attention-prediction based controller (88% success rate) outperforms the RGB-image (61% success rate) and feature-tracks (55% success rate) controller baselines. Furthermore, visual attention-prediction and feature-track based models showed better generalization performance than image-based models when evaluated on hold-out reference trajectories. Our results demonstrate that human visual attention prediction improves the performance of autonomous vision-based drone racing agents and provides an essential step towards vision-based, fast, and agile autonomous flight that eventually can reach and even exceed human performances.

cs.RO↗

arXiv2021

Expertise Affects Drone Racing Performance

First-person view drone racing has become a popular televised sport. However, very little is known about the perceptual and motor skills of professional drone racing pilots. A better understanding of these skills may inform path planning and control algorithms for autonomous multirotor flight. By using a real-world drone racing track and a large-scale position tracking system, we compare the drone racing performance of five professional and five beginner pilots. Results show that professional pilots consistently outperform beginner pilots by achieving faster lap times, higher velocity, and more efficiently executing the challenging maneuvers. Trajectory analysis shows that experienced pilots choose more optimal racing lines than beginner pilots. Our results provide strong evidence for a contribution of expertise to performances in real-world human-piloted drone racing. We discuss the implications of these results for future work on autonomous fast and agile flight. We make our data openly available.

cs.RO↗

arXiv2021

Human-Piloted Drone Racing: Visual Processing and Control

Humans race drones faster than algorithms, despite being limited to a fixed camera angle, body rate control, and response latencies in the order of hundreds of milliseconds. A better understanding of the ability of human pilots of selecting appropriate motor commands from highly dynamic visual information may provide key insights for solving current challenges in vision-based autonomous navigation. This paper investigates the relationship between human eye movements, control behavior, and flight performance in a drone racing task. We collected a multimodal dataset from 21 experienced drone pilots using a highly realistic drone racing simulator, also used to recruit professional pilots. Our results show task-specific improvements in drone racing performance over time. In particular, we found that eye gaze tracks future waypoints (i.e., gates), with first fixations occurring on average 1.5 seconds and 16 meters before reaching the gate. Moreover, human pilots consistently looked at the inside of the future flight path for lateral (i.e., left and right turns) and vertical maneuvers (i.e., ascending and descending). Finally, we found a strong correlation between pilots eye movements and the commanded direction of quadrotor flight, with an average visual-motor response latency of 220 ms. These results highlight the importance of coordinated eye movements in human-piloted drone racing. We make our dataset publicly available.

cs.RO↗