Welcome to LMNN

「微奈米製造與分析實驗室」活用在材料科學工程的兩大專長：「材料製程」與「材料分析」，致力於製造與分析技術在微米及奈米尺度下的突破。結合劉浩志老師當年在美國 Stanford University 習得之材料專業以及設計思考之能力，以及在美國高科技公司工作多年的實戰經驗，將材料科學工程的核心優勢充分跨域應用於各種新穎材料之研究：舉凡3D列印、高熵合金、掃描探針、細菌細胞及生醫材料、鈣鈦礦材料鋰離子電池、機器學習輔助材料AFM分析等，均是曾經或正在進行研究之項目。

劉老師指導學生著重設計思考能力的建立及材料專業知識之活用；基於多次跨域發展的實務經驗，劉老師除了傳授硬實力的專業知識外，也積極培養學生的軟實力，為學生的職涯長遠發展扎下厚實的根基。

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LMNN 微奈米製造與分析實驗室分析介紹

Publications

Machine learning framework for determination of elastic modulus without contact model fitting

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    • September 2022
    • International Journal of Solids and Structures  
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    • Lynn NguyenLynn Nguyen
    • Bernard Haochih LiuBernard Haochih Liu
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    • Many contact models have been proposed for determining the elastic modulus of materials based on AFM force measurement. However, contact model fitting could be a challenging task since the elastic modulus usually varies depending on the tip parameters (i.e., radius and half-opening angle) which are difficult to determine in practice. Therefore, this study proposes a supervised machine learning (SML) regression framework for determining the elastic modulus without the need to either select an appropriate contact model or perform contact model fitting. In the proposed approach, the SML regressor learns the relationship between the force-displacement (FZ) features and the elastic modulus, and then predicts the elastic modulus of unseen samples directly from their FZ features. The predictive power of the proposed framework is demonstrated using both homogeneous materials (polydimethylsiloxane, polymethyl methacrylate, and polyvinyl alcohol), and heterogeneous materials (Staphylococcus aureus bacteria and methylammonium lead iodide). It is shown that the Gaussian process regression (GPR) model achieves a higher prediction accuracy than the multiple linear regression (MLR), random forest (RF), and support vector regression (SVR) models for both groups of materials. Particularly, GPR achieves a testing coefficient of determination value of up to 91.55% for homogeneous materials, and 82.73% for heterogeneous materials. Overall, the results confirm that the proposed SML regression framework is capable of determining the elastic modulus of both homogeneous and heterogeneous materials without the need for both contact model fitting and knowledge of tip shape.
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    • https://www.sciencedirect.com/science/article/abs/pii/S0020768322004292?via%3Dihub
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Machine learning approach for reducing uncertainty in AFM nanomechanical measurements through selection of appropriate contact model

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    • July–August 2022
    • European Journal of Mechanics – A/Solids
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    • Lynn NguyenLynn Nguyen
    • Bernard Haochih LiuBernard Haochih Liu
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      • The force-displacement (FZ) curves obtained by atomic force microscopy (AFM) are one of the most commonly used methods for measuring the nanomechanical properties of engineering materials. However, the values of the elastic modulus obtained in this way inevitably contain a certain amount of uncertainty arising from the choice of contact model and the acquisition of the corresponding experimental parameters. Accordingly, this study proposes a supervised machine learning (SML) framework for selecting the appropriate contact model for evaluating the elastic modulus depending on the particular features of the FZ curve. In the proposed approach, the features of the FZ curve are extracted and supplied to the SML classifier, which learns these features and outputs the appropriate contact model accordingly.The classifier is implemented using five different classifiers and four different contact models. The SML classifiers are trained and tested using polyvinyl alcohol, polymethyl methacrylate, polydimethylsiloxane, and gold samples. The testing results show that the linear discriminant analysis (LDA) classifier provides the best contact model prediction quality for these materials. The practical feasibility of the proposed framework is demonstrated by processing the unseen FZ curves of Staphylococcus aureus bacteria. The LDA suggests the use of the modified Sneddon model with a testing accuracy of 96.8%. Overall, the results show that the proposed SML classifier provides a powerful tool for selecting the appropriate contact model and computing the corresponding elastic modulus with no prior knowledge required of the tip shape or any manual processing of the FZ curve.

        https://www.sciencedirect.com/science/article/abs/pii/S0997753822000626

Recent progress in inorganic tin perovskite solar cells

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    • January 2022
      Materials Today Energy
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    • Miaomiao Zhang , Zhiguo Zhang , Honghao Cao , Tao Zhang , Haixuan Yu , Jianying Du , Yan Shen , Xiao-Li Zhang , Jun Zhu , Peter Chen , Mingkui Wang
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    • Perovskite solar cell is a bright star in the field of photovoltaic technology. Recently, tin-based perovskites have attracted increasing attention to solve the toxicity faced by those lead halide perovskite compounds. Especially, an extensive investigation on inorganic tin perovskites with essential thermal stability and tunable optical bandgap has been initialized, and progress have been observed in photovoltaics based on them. At present, the maximum power conversion efficiency of solar cells using inorganic tin-based perovskite layer as light absorber has exceeded 10%. We notice Sn2+ oxidation is an unavoidable and critical issue for this type of device, representing unstable performing inferior to their lead-based counterparts. In this review, we mainly focus on the origin of Sn2+ oxidation for inorganic tin-based perovskites and the correlated degradation mechanism, as well as techniques to characterize their effect on device performance. We discuss several promising approaches to inhibiting the oxidation of Sn2+ and improving the stability of devices. We further outlook the effective solutions for inorganic tin-based perovskites for solar cells application with the purpose of augment photovoltaic performance, including power conversion efficiency and device stability, under working condition.
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    • https://www.sciencedirect.com/science/article/abs/pii/S246860692100256

In-Situ Investigation on Nanoscopic Biomechanics of Streptococcus mutans at Low pH Citric Acid Environments Using an AFM Fluid Cell

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    • Dec.2020
      National Institutes of Health
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    • Linh Thi Phuong Nguyen , Bernard Haochih Liu
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    • Streptococcus mutans (S. mutans) is widely regarded as the main cause of human dental caries via three main virulence factors: adhesion, acidogenicity, and aciduricity. Citric acid is one of the antibiotic agents that can inhibit the virulence capabilities of S. mutans. A full understanding of the acidic resistance mechanisms (ARMs) causing bacteria to thrive in citrate transport is still elusive. We propose atomic force microscopy (AFM) equipped with a fluid cell to study the S. mutans ARMs via surface nanomechanical properties at citric acid pH 3.3, 2.3, and 1.8. Among these treatments, at pH 1.8, the effect of the citric acid shock in cells is demonstrated through a significantly low number of high adhesion zones, and a noticeable reduction in adhesion forces. Consequently, this study paves the way to understand that S. mutans ARMs are associated with the variation of the number of adhesion zones on the cell surface, which is influenced by citrate and proton transport. The results are expected to be useful in developing antibiotics or drugs involving citric acid for dental plaque treatment.
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    • https://pubmed.ncbi.nlm.nih.gov/33322170/

Extending the limits of Pt/C catalysts with passivation-gas-incorporated atomic layer deposition

                

    • Springer Nature
    • 30 July 2018
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  •          Shicheng Xu, Yongmin Kim, Joonsuk Park, Drew Higgins, Shih-Jia Shen, Peter Schindler,    
             Dickson Thian, J. Provine, Jan Torgersen, Tanja Graf, Thomas D. Schladt, Marat Orazov, 
             Bernard Haochih Liu, Thomas F. Jaramillo & Fritz B. Prinz
    • Controlling the morphology of noble metal nanoparticles during surface depositions is strongly influenced by precursor–substrate and precursor–deposit interactions. Depositions can be improved through a variety of means, including tailoring the surface energy of a substrate to improve precursor wettability, or by modifying the surface energy of the deposits themselves.
    • Here, we show that carbon monoxide can be used as a passivation gas during atomic layer  deposition to modify the surface energy of already deposited Pt nanoparticles to assist direct deposition onto a carbon catalyst support. The passivation process promotes two-dimensional growth leading to Pt nanoparticles with suppressed thicknesses and a more than 40% improvement in Pt surface-to-volume ratio. This approach to synthesizing nanoparticulate Pt/C catalysts achieved high Pt mass activities for the oxygen reduction reaction, along with excellent stability likely facilitated by strong catalyst–support interactions afforded by this  synthetic technique.
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    • https://www.nature.com/articles/s41929-018-0118
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Extending the limits of Pt/C catalysts with passivation-gas-incorporated atomic layer deposition

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    • 1 February 2017
      Colloids and Surfaces B: Biointerfaces
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    • Bernard Haochih Liu, Li-Chieh Yu
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    • Streptococcus mutans is one of the main pathogens that cause tooth decay. By metabolizing carbohydrates, S. mutans emits extracellular polymeric substance (EPS) that adheres to the tooth surface and forms layers of biofilm. Periodontal disease occurs due to the low pH environment created by S. mutans biofilm, and such an acidic environment gradually erodes tooth enamel. Since the existence of EPS is essential in the formation of biofilm, the in-situ investigation of its generation and distribution in real time is the key to the control and suppression of S. mutans biofilm.
    • Prior studies of the biofilm formation process by fluorescence microscope, scanning electron microscope, or spectroscope have roughly divided the mechanism into three stages: (1) initial attachment; (2) microcolonies; and (3) maturation. However, these analytical methods are incapable to observe real-time changes in different locations of the extracellular matrix, and to analyze mechanical properties for single bacteria in micro and nanoscale. Since atomic force microscopy (AFM) operates by precise control of tip-sample interaction forces in liquid and in air, living microorganisms can be analyzed under near-physiological conditions. Thus, analytical techniques based on AFM constitute powerful tools for the study of biological samples, both qualitatively and quantitatively.
    • In this study, we used AFM to quantitatively track the changes of multiple nanomechanical properties of S. mutans, including dissipation energy, adhesion force, deformation, and elastic modulus at different metabolic stages. The data revealed that the bacterial extracellular matrix has a gradient distribution in stickiness, in which different stickiness indicates the variation of EPS compositions, freshness, and metabolic stages. In-situ, time-lapse AFM images showed the local generation and distribution of EPS at different times, in which the highest adhesion distributed along sides of the S. mutans cells. Through time-lapse analysis, we concluded that each contour layer is associated with a dynamic process of cell growth and nutrient consumption, and S. mutans is capable of controlling the priority of EPS secretion at specific locations. The live bacteria exhibited cyclic metabolic activities in the period of 23–34 min at the maturation stage of biofilm formation. In addition, the discharge of EPS is responsive to the shear stress caused by the topographical change of biofilm to provide stronger mechanical support in the formation of 3D networked biofilm.

                 

                   https://www.sciencedirect.com/science/article/pii/S0927776516308256