arXiv subjects

Liu Qian

Publications and source records attributed to Liu Qian.

6 recordsLinked to original sources

arXiv2026

A non-equilibrium strategy for the general synthesis of single-atom catalysts

Single-atom catalysts (SACs) maximize atom efficiency and exhibit unique electronic structures, yet realizing precise and scalable atomic dispersion remains a key challenge. Here, we report a non-equilibrium strategy for the scalable synthesis of SACs via ion implantation, enabling precise stabilization of metal atoms on diverse supports. Using an industrial-grade ion source, wafer-scale ion implantation with milliampere-level beam currents enables high-throughput fabrication of SACs, while the synergistic energy-mass effects stabilize isolated metal atoms in situ. A library of 36 SACs was constructed, and the resulting Pt/MoS2 exhibits outstanding hydrogen evolution performance with an overpotential of only 26 mV at 10 mA cm-2 and exceptional long-term stability, surpassing commercial Pt/C. This work demonstrates ion implantation as a versatile platform bridging fundamental SACs design and scalable manufacturing, providing new opportunities for high-performance catalysts in energy conversion applications.

cond-mat.mtrl-sci↗

arXiv2025

Artificial Intelligence-Enabled Holistic Design of Catalysts Tailored for Semiconducting Carbon Nanotube Growth

Catalyst design is crucial for materials synthesis, especially for complex reaction networks. Strategies like collaborative catalytic systems and multifunctional catalysts are effective but face challenges at the nanoscale. Carbon nanotube synthesis contains complicated nanoscale catalytic reactions, thus achieving high-density, high-quality semiconducting CNTs demands innovative catalyst design. In this work, we present a holistic framework integrating machine learning into traditional catalyst design for semiconducting CNT synthesis. It combines knowledge-based insights with data-driven techniques. Three key components, including open-access electronic structure databases for precise physicochemical descriptors, pre-trained natural language processing-based embedding model for higher-level abstractions, and physical - driven predictive models based on experiment data, are utilized. Through this framework, a new method for selective semiconducting CNT synthesis via catalyst - mediated electron injection, tuned by light during growth, is proposed. 54 candidate catalysts are screened, and three with high potential are identified. High-throughput experiments validate the predictions, with semiconducting selectivity exceeding 91% and the FeTiO3 catalyst reaching 98.6%. This approach not only addresses semiconducting CNT synthesis but also offers a generalizable methodology for global catalyst design and nanomaterials synthesis, advancing materials science in precise control.

cond-mat.mtrl-sci↗

arXiv2025

Subsurface Vacancy Engineering Enables Atomically Clean and Oxidation-Resistant Copper Interfaces for Anode-Free Lithium Metal Batteries

Interfaces govern reaction pathways and stability in electrochemical systems, yet creating clean, well-defined metal interfaces at scale remains challenging. In anode-free lithium metal batteries (AFLMBs), the current-collector interface is decisive for lithium nucleation and solid electrolyte interphase (SEI) formation, and ideally should support efficient charge transport, uniform reaction distribution, and long-term chemical and structural stability. Here we report an ion-implantation strategy that produces an atomically clean and oxidation-resistant copper interface. Implanting copper ions into commercial foils removes the native oxide while generating subsurface vacancy clusters directly beneath the surface -- an atomic-scale modification that does not increase the collector thickness but fundamentally alters interfacial chemistry. Experiments and multiscale simulations reveal that these vacancies act as strong oxygen traps, preventing reoxidation, enhancing interfacial conductivity, and guiding the formation of an ultrathin, Li2O-enriched SEI that promotes uniform lithium deposition and suppresses parasitic reactions. Applied in AFLMBs, the engineered current collectors deliver long-term stability with a Coulombic efficiency of 98.8% over 600 cycles under lean-electrolyte conditions. These findings demonstrate atomic-scale interface control of copper current collectors as a route toward stable and practical lithium metal batteries.

cond-mat.mtrl-sci↗

arXiv2024

Calibrated absolute optical contrast for high-throughput characterization of horizontally aligned carbon nanotube arrays

Horizontally aligned carbon nanotube (HACNT) arrays hold significant potential for various applications in nanoelectronics and material science. However, their high-throughput characterization remains challenging due to the lack of methods with both high efficiency and high accuracy. Here, we present a novel technique, Calibrated Absolute Optical Contrast (CAOC), achieved through the implementation of differential principles to filter out stray signals and high-resolution calibration to endow optical contrast with physical significance. CAOC offers major advantages over previous characterization techniques, providing consistent and reliable measurements of HACNT array density with high throughput and non-destructive assessment. To validate its utility, we demonstrate wafer-scale uniformity assessment by rapid density mapping. This technique not only facilitates the practical evaluation of HACNT arrays but also provides insights into balancing high throughput and high resolution in nanomaterial characterization.

physics.app-ph↗

arXiv2024

Transforming the Synthesis of Carbon Nanotubes with Machine Learning Models and Automation

Carbon-based nanomaterials (CBNs) are showing significant potential in various fields, such as electronics, energy, and mechanics. However, their practical applications face synthesis challenges stemming from the complexities of structural control, large-area uniformity, and high yield. Current research methodologies fall short in addressing the multi-variable, coupled interactions inherent to CBNs production. Machine learning methods excel at navigating such complexities. Their integration with automated synthesis platforms has demonstrated remarkable potential in accelerating chemical synthesis research, but remains underexplored in the nanomaterial domain. Here we introduce Carbon Copilot (CARCO), an artificial intelligence (AI)-driven platform that integrates transformer-based language models tailored for carbon materials, robotic chemical vapor deposition (CVD), and data-driven machine learning models, empowering accelerated research of CBNs synthesis. Employing CARCO, we demonstrate innovative catalyst discovery by predicting a superior Titanium-Platinum bimetallic catalyst for high-density horizontally aligned carbon nanotube (HACNT) array synthesis, validated through over 500 experiments. Furthermore, with the assistance of millions of virtual experiments, we achieved an unprecedented 56.25% precision in synthesizing HACNT arrays with predetermined densities in the real world. All were accomplished within just 43 days. This work not only advances the field of HACNT arrays but also exemplifies the integration of AI with human expertise to overcome the limitations of traditional experimental approaches, marking a paradigm shift in nanomaterials research and paving the way for broader applications.

physics.app-ph↗

arXiv2022

Ultimately-scaled electrodes for contacting individual atomically-precise graphene nanoribbons

Bottom-up synthesized graphene nanoribbons (GNRs) are quantum materials that can be structured with atomic precision, providing unprecedented control over their physical properties. Accessing the intrinsic functionality of GNRs for quantum technology applications requires the manipulation of single charges, spins, or photons at the level of an individual GNR. However, experimentally, contacting individual GNRs remains challenging due to their nanometer-sized width and length as well as their high density on the metallic growth substrate. Here, we demonstrate the contacting and electrical characterization of individual GNRs in a multi-gate device architecture using single-walled carbon nanotubes (SWNTs) as ultimately-scaled electrodes. The GNR-SWNT devices exhibit well-defined quantum transport phenomena, including Coulomb blockade, excited states, and Franck-Condon blockade, all characteristics pointing towards the contacting of an individual GNR. Combined with the multi-gate architecture, this contacting method opens a road for the integration of GNRs in quantum devices to exploit their topologically trivial and non-trivial nature.

cond-mat.mes-hall↗