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Open Access Publications from the University of California

Scholarly Works (7 results)

  • Article
  • Peer Reviewed
UC Berkeley Previously Published Works (2022)
An inexpensive virus detection scheme with high sensitivity and specificity is desirable for broad applications such as the COVID-19 virus. In this article, we introduce the localized surface plasmon resonance (LSPR) principle on the aggregation of antigen-coated gold nanoparticles (GNPs) to detect SARS-CoV-2 Nucleocapsid (N) proteins. Experiments show this technique can produce results observable by the naked eye in 5 min with a LOD (Limits of Detection) of 150 ng/ml for the N proteins. A comprehensive numerical model of the LSPR effect on the aggregation of GNPs has been developed to identify the key parameters in the reaction processes. The color-changing behaviors can be readily utilized to detect the existence of the virus while the quantitative concentration value is characterized with the assistance of an optical spectrometer. A parameter defined as the ratio of the light absorption intensity at the upper visible band region of 700 nm to the light absorption intensity at the peak optical absorption spectrum of the GNPs at 530 nm is found to have a linear relationship with respect to the N protein concentrations. As such, this scheme could be utilized as an inexpensive testing methodology for applications in POC (Point-of-Care) diagnostics to combat current and future virus-induced pandemics.
    Cover page: Gold nanoparticle based plasmonic sensing for the detection of SARS-CoV-2 nucleocapsid proteins
    • Thesis
    • Peer Reviewed
    UC Berkeley Electronic Theses and Dissertations (2024)

    Rapid advancements in artificial intelligence (AI) and deep learning have allowed computers to learn and derive novel insights from vast datasets for transformative breakthroughs. In the field of medical diagnostics, the most widely available health monitoring tools are confined to measuring vital signs like temperature, blood pressure, and heart rate without the ability to conveniently detect complex biochemistry of the human body. This dissertation bridges the gap by developing simple, affordable, and highly sensitive biosensors tailored for precise, individual-specific health monitoring based on optical methods via label-free, highly multiplexed sensing with nanometric-scale resolution.

    A colorimetric biosensing approach for at-home protein detection is developed using localized surface plasmon resonance (LSPR) on the aggregation of antigen-coated gold nanoparticles (GNPs) to detect SARS-CoV-2 Nucleocapsid (N) proteins. Experiments show this technique can produce results observable by the naked eye in 5 mins with a LOD (Limits of Detection) of 150 ng/ml for the N proteins. A comprehensive numerical model of the LSPR effect on the aggregation of GNPs has been developed to identify the key parameters in the reaction processes. An enhanced sensing mechanism that leverages the coffee-ring effect to amplify sample concentration is combined with deep neural network models. Using the evaporation of two sessile droplets and the formation of coffee-rings with asymmetric nanoplasmonic patterns, disease-relevant proteins as low as 3 pg/ml can be detected in less than 12 mins. The evaporation of the first droplet pre-concentrates proteins at the coffee ring, while the second plasmonic droplet produces a visible asymmetric plasmonic pattern by the naked-eye due to different aggregation mechanisms. The detection process is enhanced by incorporating a deep neural model that combines generative and convolutional networks for the quantitative diagnosis of biomarkers from smartphone-captured photos. Four different proteins, Procalcitonin (PCT) for sepsis, SARS-CoV-2 Nucleocapsid protein (N-Protein) for COVID-19, Carcinoembryonic antigen (CEA) and Prostate-specific antigen (PSA) for cancer diagnosis have been tested for a wide working concentration range over 5-order of magnitude.

    A more sensitive platform by arranging plasmonic nanoparticles into 2D lattices to achieve exceptional points (EPs) is established to enable ultra-sensitive detections of nucleic acids. To enhance the robustness and stability of these EP biosensors, an AI-assisted phase-sensing methodology is incorporated with tunable parameters for post-fabrication calibration, achieving LOD of 1 fM in 30 min for nCoVE gene of SARS-CoV-2 diagnosis, about order of magnitude better than the state-of-the-art rapid photonic sensors. Finally, the integration of single-particle EP biosensing with photonic integrated circuit (PIC) interferometers is accomplished to achieve a compact, scalable design, enabling single-molecule detection. In conclusion, this research combines advanced plasmonic techniques with deep learning and phase sensing to address critical challenges in medical diagnostics. By bridging the gap between sensitivity, affordability, and accessibility, these innovations lay the groundwork for more personalized and precise health monitoring solutions.

      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2022)
      We present a novel systematic MEMS structure design approach based on a 'deep conditional generative model'. Utilizing the conditional generative adversarial network (CGAN) on a case study of circular-shaped MEMS resonators, three major advancements have been demonstrated: 1) a high-throughput vectorized MEMS design generation scheme that satisfies the geometric constraints; 2) MEMS structural customization toward tunable, desired physical properties with excellent generation accuracy; and 3) experience-free design space explorations to achieve extreme physical properties, such as low anchor loss of micro resonators. Excellent agreements with experimental data, numerical ulations, and a previously reported machine learning-based analyzer are achieved for validation of our methodology. As such, the proposed scheme could open up a new class of data-driven, intelligent design systems for a wide range of MEMS applications.
        Cover page: Customizing Mems Designs via Conditional Generative Adversarial NetworksCreative Commons 'BY' version 4.0 license
        • Article
        • Peer Reviewed
        UC Berkeley Previously Published Works (2022)
        We demonstrate a design scheme for weakly coupled resonators (WCRs) by integrating the supervised learning (SL) with the genetic algorithm (GA). In this work, three distinctive achievements have been accomplished: 1) the precise prediction of coupling characteristics of WCRs with an accuracy of 98.7% via SL; 2) the stepwise evolutionary optimization of WCR geometries while maintaining their geometric connectivity via GA; and 3) the highly efficient generation of WCR designs with a mean coupling factor down to 0.0056, which outperforms 98% of random designs. The coupling behavior analysis and prediction are validated with experimental data of coupled microcantilevers from a published work. As such, this newly proposed scheme could shed light upon the structural optimization methods for high-performance MEMS devices with high degree of design freedom.
          Cover page: Designing Weakly Coupled Mems Resonators with Machine Learning-Based MethodCreative Commons 'BY' version 4.0 license
          • Article
          • Peer Reviewed
          UC Berkeley Previously Published Works (2025)
          The miniaturization of insect-scale flying robots with untethered flights is extremely challenging as the tradeoff between mass and power becomes problematic. Here, a subcentimeter rotating-wing robot of 21 mg in weight and 9.4 mm in wingspan driven by a single-axis alternating magnetic field has accomplished navigable flights. This artificial flying robot is the lightest and smallest to realize untethered and controllable aerial travels including hovering, collision recovery, and route adjustments. Experimentally, it has achieved a high aerodynamic efficacy with a measured lift-to-drag ratio of 0.7 and lift-to-flying power ratio of 7.2 × 10-2 N/W at a Reynolds number of ~2500. The wireless driving mechanism, system operation principle, and flight characteristics can be further optimized for the advancement and miniaturization of subcentimeter scale flying robots.
            Cover page: Untethered subcentimeter flying robots.Creative Commons 'BY-NC' version 4.0 license
            • Article
            • Peer Reviewed
            UC San Francisco Previously Published Works (2023)
            The papain-like protease (PLpro) plays a critical role in SARS-CoV-2 (SCoV-2) pathogenesis and is essential for viral replication and for allowing the virus to evade the host immune response. Inhibitors of PLpro have great therapeutic potential, however, developing them has been challenging due to PLpro's restricted substrate binding pocket. In this report, we screened a 115 000-compound library for PLpro inhibitors and identified a new pharmacophore, based on a mercapto-pyrimidine fragment that is a reversible covalent inhibitor (RCI) of PLpro and inhibits viral replication in cells. Compound 5 had an IC50 of 5.1 μM for PLpro inhibition and hit optimization yielded a derivative with increased potency (IC50 0.85 μM, 6-fold higher). Activity based profiling of compound 5 demonstrated that it reacts with PLpro cysteines. We show here that compound 5 represents a new class of RCIs, which undergo an addition elimination reaction with cysteines in their target proteins. We further show that their reversibility is catalyzed by exogenous thiols and is dependent on the size of the incoming thiol. In contrast, traditional RCIs are all based upon the Michael addition reaction mechanism and their reversibility is base-catalyzed. We identify a new class of RCIs that introduces a more reactive warhead with a pronounced selectivity profile based on thiol ligand size. This could allow the expansion of RCI modality use towards a larger group of proteins important for human disease.
              Cover page: Mercapto-pyrimidines are reversible covalent inhibitors of the papain-like protease (PLpro) and inhibit SARS-CoV-2 (SCoV-2) replicationCreative Commons 'BY' version 4.0 license
              • Article
              • Peer Reviewed
              UC San Francisco Previously Published Works (2023)
              Covalent inhibitors of the papain-like protease (PLpro) from SARS-CoV-2 have great potential as antivirals, but their non-specific reactivity with thiols has limited their development. In this report, we performed an 8000 molecule electrophile screen against PLpro and identified an α-chloro amide fragment, termed compound 1, which inhibited SARS-CoV-2 replication in cells, and also had low non-specific reactivity with thiols. Compound 1 covalently reacts with the active site cysteine of PLpro, and had an IC50 of 18 μM for PLpro inhibition. Compound 1 also had low non-specific reactivity with thiols and reacted with glutathione 1-2 orders of magnitude slower than other commonly used electrophilic warheads. Finally, compound 1 had low toxicity in cells and mice and has a molecular weight of only 247 daltons and consequently has great potential for further optimization. Collectively, these results demonstrate that compound 1 is a promising lead fragment for future PLpro drug discovery campaigns.
                Cover page: A covalent inhibitor targeting the papain-like protease from SARS-CoV-2 inhibits viral replicationCreative Commons 'BY' version 4.0 license
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