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1. Origami-based integration of robots that sense, decide, and respond

   https://doi.org/10.1038/s41467-023-37158-9  

   Yan, Wenzhong ; Li, Shuguang ; Deguchi, Mauricio ; Zheng, Zhaoliang ; Rus, Daniela ; Mehta, Ankur ( April 2023 , Nature Communications)

   Origami-inspired engineering has enabled intelligent materials and structures to process and react to environmental stimuli. However, it is challenging to achieve complete sense-decide-act loops in origami materials for autonomous interaction with environments, mainly due to the lack of information processing units that can interface with sensing and actuation. Here, we introduce an integrated origami-based process to create autonomous robots by embedding sensing, computing, and actuating in compliant, conductive materials. By combining flexible bistable mechanisms and conductive thermal artificial muscles, we realize origami multiplexed switches and configure them to generate digital logic gates, memory bits, and thus integrated autonomous origami robots. We demonstrate with a flytrap-inspired robot that captures ‘living prey’, an untethered crawler that avoids obstacles, and a wheeled vehicle that locomotes with reprogrammable trajectories. Our method provides routes to achieve autonomy for origami robots through tight functional integration in compliant, conductive materials.

    

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2. Replication Data for: Multi-Point Nanoindentation Method to Determine Mechanical Anisotropy in Nanofibrillar Thin Films

   https://doi.org/10.7910/DVN/LUEV5J  

   Perera, Dinidu ; Wang, Qijue ; Schniepp, Hannes C. ( January 2022 , Harvard Dataverse)

   Raw data of scanning electron microscopy (SEM), atomic force microscopy (AFM), force spectroscopy, data analysis and plotting, optical microscopy, and finite element simulations (FEA) for our manuscript. File Formats AFM raw data is provided in Gwyddion format, which can be viewed using the Gwyddion AFM viewer, which has been released under the GNU public software licence GPLv3 and can be downloaded free of charge at http://gwyddion.net/ Optical microscopy data is provided in JPEG format SEM raw data is provided in TIFF format Data analysis codes were written in MATLAB (https://www.mathworks.com/products/matlab) and stored as *.m files Imported raw data to MATLAB and saved MATLAB data were stored as MATLAB multidimensional arrays (MATLAB “struct” data format, *.mat files) FEA results were saved as text files, .txt files) Data (Folder Structure) The data in the dataverse is best viewed in Tree mode. Read me file.docx More Explanations of analysis in docx format. Figure 1 Figure 1 - panel b.jpg (5.5 MB) Optical micrograph (JPEG format) Figure 1 - panel c - AFM Raw Data.gwy (8.0 MB) AFM raw data (Gwyddion format) Figure 1 - panel e - P0_Force-curve_raw_data.txt (3 KB) Raw force-displacement data at P0 (text format) Figure 1 - panel e - Px_Force-curve_raw_data.txt (3 KB) Raw force-displacement data at Px (text format) Figure 1 - panel e - Py_Force-curve_raw_data.txt (3 KB) Raw force-displacement data at Py (text format) Figure 1 - panel e - P0_simulation_raw_data.txt (12 KB) FEA simulated force-distance data at P0 (text format) Figure 1 - panel e - Px_simulation_raw_data.txt (12 KB) FEA simulated force-distance data at Px (text format) Figure 1 - panel e - Py_simulation_raw_data.txt (12 KB) FEA simulated force-distance data at Py (text format) Figure 1 - panel e - FCfindc.m (2 KB) MATLAB code to calculate inverse optical lever sensitivity (InverseOLS) of AFM cantelever (matlab .m format) Figure 1 - panel e - FreqFindANoise_new.m (2 KB) MATLAB code to calculate white noise constant, A (Explained in the Read me file) (matlab .m format) Figure 1 - panel e - FreqFindQ_new.m (4 KB) MATLAB code to calculate Q factor of the AFM cantelever (matlab .m format) Figure 1 - panel e - FCkeff.m (2 KB) MATLAB code to calculate the effective spring constant k of the AFM cantelever (matlab .m format) Figure 1 - panel e - FCimport.m (7 KB) MATLAB code to import raw force-displacement data into MATLAB (matlab .m format) Figure 1 - panel e - FCForceDist.m (2 KB) MATLAB code to convert raw force-displacement data into force-distance data (matlab .m format) Figure 1 - panel e - Figure 1- Panel e - data.mat (6 KB) MATALB struct data file for calibrated force-distance data at all indentation points (matlab .mat format) Figure 1 - panel e - Panel_e_MatlabCode.m (6 KB) MATALB code for plotting experimental and simulated force curves in panel e (matlab .m format) Figure 1 - panel e - Read me file - force curve calibration.docx (14 KB) Explains force curve calibration (.docx format) Figure 1 - panel e - Read me file - lever spring constant calibration.docx (14 KB) Explains AFM lever spring constant calibration (.docx format) Figure 2 Figure 2 - panel a - MATLAB data.mat (2.6 KB) MATALB data file for simulated data (matlab .mat format) Figure 2 - panel b - MATLAB data.mat (2.4 KB) MATALB data file for simulated data (matlab .mat format) Figure 2 - panel a - simulation raw data.txt (5.0 KB) Raw simulation data: xyz coordinates of the nodes of deformed FEA mesh (text format) Figure 2 - panel b - simulation raw data.txt (5.0 KB) Raw simulation data: xyz coordinates of the nodes of deformed FEA mesh (text format) Figure 2 - panel ab - MATLABcode.m (1.0 KB) MATALB code for plotting panel a b figures (matlab .m format) Figure 2 - panel c - Degree of Anisotropy datacode.m (1.0 KB) MATALB code for plotting panel c graph (matlab .m format) Figure 3 Figure 3 - panel a - App_curve_1_raw_data.txt (35 KB) Raw force-displacement data approach curve 1 (text format) Figure 3 - panel a - App_curve_2_raw_data.txt (34 KB) Raw force-displacement data approach curve 2 (text format) Figure 3 - panel a - App_curve_3_raw_data.txt (34 KB) Raw force-displacement data approach curve 3 (text format) Figure 3 - panel a - App_curve_4_raw_data.txt (34 KB) Raw force-displacement data approach curve 4 (text format) Figure 3 - panel a - Ret_curve_1_raw_data.txt (35 KB) Raw force-displacement data of retract curve 1 (text format) Figure 3 - panel a - Ret_curve_2_raw_data.txt (35 KB) Raw force-displacement data of retract curve 2 (text format) Figure 3 - panel a - Ret_curve_3_raw_data.txt (35 KB) Raw force-displacement data of retract curve 3 (text format) Figure 3 - panel a - Simulation_raw_data-part 1.txt (43 KB) simulated force-displacement data of -part 1 (text format) Figure 3 - panel a - Simulation_raw_data-part 2.txt (43 KB) simulated force-displacement data of -part 2 (text format) Figure 3 - panel a - FCfindc.m (2 KB) MATLAB code to calculate inverse optical lever sensitivity (InverseOLS) of AFM cantelever (matlab .m format) Figure 3 - panel a - FreqFindANoise_new.m (2 KB) MATLAB code to calculate white noise constant, A (Explained in the Read me file) (matlab .m format) Figure 3 - panel a - FreqFindQ_new.m (4 KB) MATLAB code to calculate Q factor of the AFM cantelever (matlab .m format) Figure 3 - panel a - FCkeff.m (2 KB) MATLAB code to calculate the effective spring constant k of the AFM cantelever (matlab .m format) Figure 3 - panel a - FCimport.m (7 KB) MATLAB code to import raw force-displacement data into MATLAB (matlab .m format) Figure 3 - panel a - FCForceDist.m (2 KB) MATLAB code to convert raw force-displacement data into force-distance data (matlab .m format) Figure 1 - panel e - Read me file - force curve calibration.docx (14 KB) Explains force curve calibration (.docx format) Figure 1 - panel e - Read me file - lever spring constant calibration.docx (14 KB) Explains AFM lever spring constant calibration (.docx format) Figure 3 - panel b - SEM Raw Data.tiff (9 KB) SEM raw image of broken silk membrane due to extreme indentation (.tiff format) 

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3. Simulation Data for Design of Peptides that Fold and Self-Assemble on Graphite

   https://doi.org/10.5281/zenodo.6426152  

   Comer, Jeffrey ; Thakkar, Ravindra ; Velásquez-Silva, Astrid ; Miranda-Carvajal, Ingrid ( January 2022 , Zenodo)

   This data set for the manuscript entitled "Design of Peptides that Fold and Self-Assemble on Graphite" includes all files needed to run and analyze the simulations described in the this manuscript in the molecular dynamics software NAMD, as well as the output of the simulations. The files are organized into directories corresponding to the figures of the main text and supporting information. They include molecular model structure files (NAMD psf or Amber prmtop format), force field parameter files (in CHARMM format), initial atomic coordinates (pdb format), NAMD configuration files, Colvars configuration files, NAMD log files, and NAMD output including restart files (in binary NAMD format) and trajectories in dcd format (downsampled to 10 ns per frame). Analysis is controlled by shell scripts (Bash-compatible) that call VMD Tcl scripts or python scripts. These scripts and their output are also included.

   Version: 2.0

   Changes versus version 1.0 are the addition of the free energy of folding, adsorption, and pairing calculations (Sim_Figure-7) and shifting of the figure numbers to accommodate this addition.

   
   Conventions Used in These Files
   ===============================

   Structure Files
   ----------------
   - graph_*.psf or sol_*.psf (original NAMD (XPLOR?) format psf file including atom details (type, charge, mass), as well as definitions of bonds, angles, dihedrals, and impropers for each dipeptide.)

   - graph_*.pdb or sol_*.pdb (initial coordinates before equilibration)
   - repart_*.psf (same as the above psf files, but the masses of non-water hydrogen atoms have been repartitioned by VMD script repartitionMass.tcl)
   - freeTop_*.pdb (same as the above pdb files, but the carbons of the lower graphene layer have been placed at a single z value and marked for restraints in NAMD)
   - amber_*.prmtop (combined topology and parameter files for Amber force field simulations)
   - repart_amber_*.prmtop (same as the above prmtop files, but the masses of non-water hydrogen atoms have been repartitioned by ParmEd)

   Force Field Parameters
   ----------------------
   CHARMM format parameter files:
   - par_all36m_prot.prm (CHARMM36m FF for proteins)
   - par_all36_cgenff_no_nbfix.prm (CGenFF v4.4 for graphene) The NBFIX parameters are commented out since they are only needed for aromatic halogens and we use only the CG2R61 type for graphene.
   - toppar_water_ions_prot_cgenff.str (CHARMM water and ions with NBFIX parameters needed for protein and CGenFF included and others commented out)

   Template NAMD Configuration Files
   ---------------------------------
   These contain the most commonly used simulation parameters. They are called by the other NAMD configuration files (which are in the namd/ subdirectory):
   - template_min.namd (minimization)
   - template_eq.namd (NPT equilibration with lower graphene fixed)
   - template_abf.namd (for adaptive biasing force)

   Minimization
   -------------
   - namd/min_*.0.namd

   Equilibration
   -------------
   - namd/eq_*.0.namd

   Adaptive biasing force calculations
   -----------------------------------
   - namd/eabfZRest7_graph_chp1404.0.namd
   - namd/eabfZRest7_graph_chp1404.1.namd (continuation of eabfZRest7_graph_chp1404.0.namd)

   Log Files
   ---------
   For each NAMD configuration file given in the last two sections, there is a log file with the same prefix, which gives the text output of NAMD. For instance, the output of namd/eabfZRest7_graph_chp1404.0.namd is eabfZRest7_graph_chp1404.0.log.

   Simulation Output
   -----------------
   The simulation output files (which match the names of the NAMD configuration files) are in the output/ directory. Files with the extensions .coor, .vel, and .xsc are coordinates in NAMD binary format, velocities in NAMD binary format, and extended system information (including cell size) in text format. Files with the extension .dcd give the trajectory of the atomic coorinates over time (and also include system cell information). Due to storage limitations, large DCD files have been omitted or replaced with new DCD files having the prefix stride50_ including only every 50 frames. The time between frames in these files is 50 * 50000 steps/frame * 4 fs/step = 10 ns. The system cell trajectory is also included for the NPT runs are output/eq_*.xst.

   Scripts
   -------
   Files with the .sh extension can be found throughout. These usually provide the highest level control for submission of simulations and analysis. Look to these as a guide to what is happening. If there are scripts with step1_*.sh and step2_*.sh, they are intended to be run in order, with step1_*.sh first.

   
   CONTENTS
   ========

   The directory contents are as follows. The directories Sim_Figure-1 and Sim_Figure-8 include README.txt files that describe the files and naming conventions used throughout this data set.

   Sim_Figure-1: Simulations of N-acetylated C-amidated amino acids (Ac-X-NHMe) at the graphite–water interface.

   Sim_Figure-2: Simulations of different peptide designs (including acyclic, disulfide cyclized, and N-to-C cyclized) at the graphite–water interface.

   Sim_Figure-3: MM-GBSA calculations of different peptide sequences for a folded conformation and 5 misfolded/unfolded conformations.

   Sim_Figure-4: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 370 K.

   Sim_Figure-5: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 295 K.

   Sim_Figure-5_replica: Temperature replica exchange molecular dynamics simulations for the peptide cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) with 20 replicas for temperatures from 295 to 454 K.

   Sim_Figure-6: Simulation of the peptide molecule cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) in free solution (no graphite).

   Sim_Figure-7: Free energy calculations for folding, adsorption, and pairing for the peptide CHP1404 (sequence: cyc(GTGSGTG-GPGG-GCGTGTG-SGPG)). For folding, we calculate the PMF as function of RMSD by replica-exchange umbrella sampling (in the subdirectory Folding_CHP1404_Graphene/). We make the same calculation in solution, which required 3 seperate replica-exchange umbrella sampling calculations (in the subdirectory Folding_CHP1404_Solution/). Both PMF of RMSD calculations for the scrambled peptide are in Folding_scram1404/. For adsorption, calculation of the PMF for the orientational restraints and the calculation of the PMF along z (the distance between the graphene sheet and the center of mass of the peptide) are in Adsorption_CHP1404/ and Adsorption_scram1404/. The actual calculation of the free energy is done by a shell script ("doRestraintEnergyError.sh") in the 1_free_energy/ subsubdirectory. Processing of the PMFs must be done first in the 0_pmf/ subsubdirectory. Finally, files for free energy calculations of pair formation for CHP1404 are found in the Pair/ subdirectory.

   Sim_Figure-8: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) where the peptides are far above the graphene–water interface in the initial configuration.

   Sim_Figure-9: Two replicates of a simulation of nine peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 370 K.

   Sim_Figure-9_scrambled: Two replicates of a simulation of nine peptide molecules with the control sequence cyc(GGTPTTGGGGGGSGGPSGTGGC) at the graphite–water interface at 370 K.

   Sim_Figure-10: Adaptive biasing for calculation of the free energy of the folded peptide as a function of the angle between its long axis and the zigzag directions of the underlying graphene sheet.

    

   This material is based upon work supported by the US National Science Foundation under grant no. DMR-1945589. A majority of the computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CHE-1726332, CNS-1006860, EPS-1006860, and EPS-0919443. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562, through allocation BIO200030. 

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4. In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

   https://doi.org/10.1007/s10845-022-01977-2  

   Gaikwad, Aniruddha ; Chang, Tammy ; Giera, Brian ; Watkins, Nicholas ; Mukherjee, Saptarshi ; Pascall, Andrew ; Stobbe, David ; Rao, Prahalada ( June 2022 , Journal of Intelligent Manufacturing)

   In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.

    

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5. Characterization of Bioengineered Tissues by Digital Holographic Vibrometry and 3D Shape Deep Learning

   Hiscox, C. ; Li, J. ; Gao, Z. ; Korkin, D. ; Furlong, C. ; Billiar, K. ( July 2023 , 2022 Conference Proceedings of the Society for Experimental Mechanics Series)

   Lin, MT. ; Furlong, C. ; Hwang, CH. ; Naraghi, M. ; DelRio, F. (Ed.)

   Tissue engineering is an active field and one of its aims is to produce tissues to repair the human body. The Advanced Regenerative Medicine Initiative (ARMI) currently seeks to help increase the manufacturability of tissue engineering products (TEMPs). One of the critical components of large-scale manufacturing is the sensing of information for quality control and critical feedback of tissue growth patterns. Modern sensors that provide information about physical qualities of tissues, however, are invasive or destructive. The goal of this project is to develop noninvasive methodologies to measure the mechanical properties of TEMPs. Our approach is to utilize acoustic waves to induce nano-scale level vibrations in the enginineered tissues in which corresponding displacements are measured in full-field with quantitative optical techniques. In our work, a digital holographic system images the tissue’s vibration at significant modes and provides the displacement patterns of the tissue at various points along the sinusoidal excitation curve. These data are applied to a neural network to compare the experimental vibrational modes to the ones obtained by FEA simulation to estimate the physical properties of the tissue. This methodology has the promise of yielding critical control parameters that would allow technicians to noninvasively and consistently determine when samples are ready to be packaged or if their growth deviates from expected time frames or if there are defects in the tissue. It is expected that this approach will streamline several components of the quality control and production process. 

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