About

Liwei Lin is a Distinguished Professor of Mechanical Engineering at the University of California, Berkeley, and the James Marshall Wells Academic Chair. His research focuses on MEMS (Microelectromechanical Systems), NEMS (Nanoelectromechanical Systems), nanotechnology, and the design and manufacturing of microsensors and microactuators. He specializes in the development of micromachining processes, including silicon surface and bulk micromachining, micromolding processes, and addresses mechanical issues in MEMS such as heat transfer, solid and fluid mechanics, and dynamics. Professor Lin has held various academic positions at UC Berkeley, including Vice Chair for Graduate Study in the Mechanical Engineering Department from 2006 to 2009. He earned his PhD in Mechanical Engineering from UC Berkeley in 1993, following his MS from the same institution in 1991 and a BS in Power Mechanical Engineering from National Tsing Hua University in 1986. His professional background also includes positions at the University of Michigan and National Taiwan University, as well as industry experience as a Senior Research Scientist at BEI Electronics Inc. His contributions to the field are recognized through his leadership roles and his extensive research in micro- and nano-scale mechanical systems.

Research topics

• Computer Science
• Nanotechnology
• Materials science
• Computer Security
• Engineering
• Organic chemistry
• Chemical engineering
• Biochemical engineering
• Chemistry
• Manufacturing engineering
• Mechanical engineering

Selected publications

• Multiple synergistic effects induced by Ge doping enhance the thermoelectric performance of n-type PbTe

  Journal of Materials Chemistry A · 2026-01-01

  article

  Cu/Ge co-doping in n-type PbTe enlarges interstitial voids, enhances Cu solubility, flattens bands, and softens the off-centered lattice, achieving peak zT ∼ 1.4, zT avg ∼ 1.0 (300–773 K), and ∼13% theoretical efficiency.

  PublisherDOI

• Risk-adaptive therapy guided by dynamic ctDNA in nasopharyngeal carcinoma

  Nature · 2026-03-11 · 2 citations

  article
  PublisherDOI

• Cyclodextrin metal-organic frameworks as templates for ordered polymerisation of polyaniline in supercapacitors with enhanced electrochemical performance

  Supramolecular chemistry · 2025-06-23 · 1 citations

  article

  A cyclodextrin metal-organic framework (CD-MOF) was used as a synthesis template to guide the ordered polymerisation of polyaniline (PANI). The resulting PANI/CD-MOF composites (PM) were successfully prepared via interfacial polymerisation, with their structure and properties regulated by adjusting the concentrations of aniline and CD-MOF. The electrochemical performance of PM was evaluated using cyclic voltammetry (CV), galvanostatic charge–discharge (GCD), and electrochemical impedance spectroscopy (EIS). Experimental results demonstrated that the material exhibited excellent electrochemical performance in supercapacitors. Specifically, when the aniline concentration was 0.04 mol/L, PM-0.04 achieved a capacitance of 628.8 F/g at 1 A/g. Furthermore, a symmetric supercapacitor assembled with PM-0.04 exhibited a specific capacitance of 119.4 F/g at 1 A/g, along with good cycling stability at 5 A/g. These results demonstrate the potential of CD-MOF-based composites in enhancing the performance of supercapacitors, offering high specific capacitance, good cycling stability, and rapid charge–discharge capability.

  PublisherDOI

• Laser-assisted direct three-dimensional printing of free-standing thermoset devices

  Nature Electronics · 2025-11-07 · 6 citations

  article
  PublisherDOI

• A 3.55‐µm Ultrathin, Skin‐Like Mechanoresponsive, Compliant, and Seamless Ionic Conductive Electrode for Epidermal Electrophysiological Signal Acquisition and Human‐Machine Interaction

  Exploration · 2025-07-08 · 17 citations

  articleOpen access

  Flexible ionic conductive electrodes, as a fundamental component for electrical signal transmission, play a crucial role in skin-surface electronic devices. Developing a skin-seamlessly electrode that can effectively capture long-term, artifact-free, and high-quality electrophysiological signals remains a challenge. Herein, we report an ultra-thin and dry electrode consisting of deep eutectic solvent (DES) and zwitterions (CEAB), which exhibit significantly lower reactance and noise in both static and dynamic monitoring compared to standard Ag/AgCl gel electrodes. Our electrodes have skin-like mechanical properties (strain-rigidity relationship and flexibility), outstanding adhesion, and high electrical conductivity. Consequently, they excel in consistently capturing high-quality epidermal biopotential signals, such as the electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) signals. Furthermore, we demonstrate the promising potential of the electrodes in clinical applications by effectively distinguishing aberrant EEG signals associated with depressive patients. Meanwhile, through the integration of CEAB electrodes with digital processing and advanced algorithms, valid gesture control of artificial limbs based on EMG signals is achieved, highlighting its capacity to significantly enhance human-machine interaction.

  PublisherOA PDFDOI

• [The advances of artificial intelligence in the diagnosis and treatment of nasopharyngeal carcinoma].

  PubMed · 2025-12-07

  article
  PublisherDOI

• Hypersensitive pressure sensors inspired by scorpion mechanosensory mechanisms for near-body flow detection in intelligent robots

  Science Advances · 2025-08-20 · 11 citations

  articleOpen access

  Sensitivity enhancement for pressure sensors over a broad linear range can improve sensing performance for a wide range of applications such as health monitoring and artificial intelligence. Here, inspired by the high-precision mechanosensory mechanism of the scorpion, a bioinspired piezoresistive pressure sensor (BPPS) is reported for the synergistic enhancement of sensitivity and linearity at 65.56 millivolts per volt per kilopascal and 0.99934, respectively, in a pressure range from 0 to 500 kilopascals. The BPPS can distinguish laminar, transitional, and turbulent flows as well as identify approaching objects of different shapes with an accuracy exceeding 85.42% by integrating a wavelet transform algorithm and the ResNet18 deep learning network. As a proof of concept, BPPS has been engineered in a hexapod robot to enable near-body flow field sensing for active collision avoidance. This work underscores the potential to leverage key design concepts inspired by living insects for improved sensing performance and offers structural insights for other high-precision sensors.

  PublisherDOI

• The Perspectives of Families in Decision-Making Conflicts Related to Palliative Care Patients With Mechanical Ventilation in Taiwan

  American Journal of Hospice and Palliative Medicine® · 2025-05-11

  articleOpen access

  BackgroundFamily members experience decision-making conflicts regarding changes in patient care. If the medical team does not attempt to understand the family members' awareness of palliative care, family members may experience medical decision-making stress and dilemmas. This study examined the decision-making conflicts of the family members of patients dependent on prolonged mechanical ventilator regarding palliative care in Taiwan.MethodsA cross-sectional design was used in this study. Family members of such patients in the subacute respiratory care ward and the respiratory intensive care unit of a medical center in Taiwan were recruited. A structured questionnaire was used to collect data.ResultsAmong the family members of the 127 patients included, 57.5% hesitated to make palliative medical decisions and 61.4% experienced palliative medical decision conflicts. The absence of other chronic diseases, family members' inability to accept the movement of patients to palliative care, and family members' hesitation in palliative care medical decision-making resulted in decision-making conflicts. In this study, 127 prolonged mechanical ventilation-dependent patients (PMVDP) and their family members were examined. The results revealed that family members experienced palliative care medical decision-making difficulties (61.40% = <2.5). Predictors of palliative care decision-making conflict for PMVDP and their family members included the following: absence of other comorbid chronic diseases, the inability of family to accept palliative care on behalf of patients, and hesitation in palliative care medical decision-making by family members.ConclusionThe study results are able to help Taiwanese medical staff in evaluating such conflicts and palliative care medical decisions of PMVDP.

  PublisherOA PDFDOI

• Versatile Symbolic Music-for-Music Modeling via Function Alignment

  ArXiv.org · 2025-06-18

  preprintOpen access

  Many music AI models learn a map between music content and human-defined labels. However, many annotations, such as chords, can be naturally expressed within the music modality itself, e.g., as sequences of symbolic notes. This observation enables both understanding tasks (e.g., chord recognition) and conditional generation tasks (e.g., chord-conditioned melody generation) to be unified under a music-for-music sequence modeling paradigm. In this work, we propose parameter-efficient solutions for a variety of symbolic music-for-music tasks. The high-level idea is that (1) we utilize a pretrained Language Model (LM) for both the reference and the target sequence and (2) we link these two LMs via a lightweight adapter. Experiments show that our method achieves superior performance among different tasks such as chord recognition, melody generation, and drum track generation. All demos, code and model weights are publicly available.

  PublisherOA PDFDOI

• Highly Responsive Self‐Healing and Degradable Piezoelectric Soft Machines

  Advanced Materials · 2025-07-14 · 15 citations

  articleSenior authorCorresponding

  Abstract Piezoelectric materials that are simultaneously healable, stretchable, and degradable have remained an unmet challenge, limiting advancements in wearable and implantable electronics, where devices face multidimensional mechanical deformation, causing a risk of damage. To address this critical gap, a biocompatible piezoelectric material is developed for ultrahigh piezoelectric effects with DL‐alanine amino acid crystals, which is stretchable, healable, and degradable. The in situ grown DL‐alanine piezoelectric crystals within an ionically cross‐linked gelatin hydrogel matrix strengthen the piezoelectric properties with an ultrahigh voltage coefficient of 1.6 Vm N −1 . The combination of the piezo‐ionic property and crystal alignment results in a record‐breaking energy harvesting figure‐of‐merit value at 57.6 pm 2 N −1 to deliver outstanding mili‐watt level power outputs in proof‐of‐concept devices which can power up even several electric light bulbs. An elastically stretchable, damage resistant strain sensor is further optimized for real‐time healthcare monitoring and biomechanical motion tracking. By integrating machine learning algorithms, the sensing system intelligently classifies biomechanical activities with high accuracy, enabling advanced applications in healthcare, rehabilitation, and sports monitoring.

  PublisherDOI

Recent grants

• Electrically Tunable Graphene Gas Sensors

  NSF · $330k · 2017–2020

• Electrospun Piezoelectric Nanogenerator

  NSF · $350k · 2009–2013

• Electrosynthesized Nanocomposite for Microelectromechanical Systems

  NSF · $210k · 2004–2007

• Direct Synthesis, Assembly and Integration of Graphene via Micro CVD

  NSF · $401k · 2010–2014

• CAREER: MEMS Post-Packaging by Localized Heating

  NSF · $200k · 1998–2002

Frequent coauthors

• Shu‐Hui Yeh

  Mackay Medical College

  183 shared

• Xiaohao Wang

  Tsinghua–Berkeley Shenzhen Institute

  125 shared

• Junwen Zhong

  111 shared

• Yao Chu

  Shanghai Normal University

  90 shared

• Yichuan Wu

  University of Electronic Science and Technology of China

  84 shared

• Xining Zang

  73 shared

• Huiliang Liu

  China National Space Administration

  72 shared

• Chiu‐Yueh Hsiao

  Chang Gung University

  71 shared

Labs

• Lin LabPI

Education

• Ph.D., Mechanical Engineering

  University of California, Berkeley

  2002

• M.S., Mechanical Engineering

  University of California, Berkeley

  1999

• B.S., Mechanical Engineering

  University of Science and Technology of China

  1996