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1. Generating the Gopher’s Grounds: Form, Function, Order, and Alignment

   https://doi.org/10.1007/978-3-031-22953-4_7  

   Zhao, Jiayi; Kamath, Anshul; Grisanti, Nick; Montanez, George D (January 2023, Agents and Artificial Intelligence. ICAART 2022. Lecture Notes in Computer Science (vol 13786))

   Rocha, A.P.; Steels, L.; van den Herik, J. (Ed.)

   Previous work has shown that artificial agents with the ability to discern function from structure (intention perception) in simple combinatorial machines possess a survival advantage over those that cannot. We seek to examine the strength of the relationship between structure and function in these cases. To do so, we use genetic algorithms to generate simple combinatorial machines (in this case, traps for artificial gophers). Specifically, we generate traps both with and without structure and function, and examine the correlation between trap coherence and lethality, the capacity of genetic algorithms to generate lethal and coherent traps, and the information resources necessary for genetic algorithms to create traps with specified traits. We then use the traps generated by the genetic algorithms to see if artificial agents with intention perception still possess a survival advantage over those that do not. Our findings are two-fold. First, we find that coherence (structure) is much harder to achieve than lethality (function) and that optimizing for one does not beget the other. Second, we find that agents with intention perception do not possess strong survival advantages when faced with traps generated by a genetic algorithm. 

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2. Trap-dominated nitrogen dioxide and ammonia responses of air-stable p-channel conjugated polymers from detailed bias stress analysis

   https://doi.org/10.1039/D0TC05458E  

   Mukhopadhyaya, Tushita; Katz, Howard E. (March 2021, Journal of Materials Chemistry C)

   null (Ed.)

   The improvement of conjugated polymer-based gas sensors involves fine tuning the backbone electronic structure and solid-state microstructure to combine high stability and sensitivity. We had previously developed a series of diketopyrrolopyrrole (DPP)-based polymer semiconductors by introducing a variety of fluorene linkers to study the trends and mechanisms governing gas sensitivities and electronic stability in air and under gate and drain bias stress. The proportional on-current change of organic field-effect transistors (OFETs) using a dithienyl DPP–fluorene polymer reached ∼600% for a sequential exposure from 0.5–20 ppm of NO 2 for 5 minutes and also a high response-to-drift ratio under dynamic bias stress. In the present work we specify the roles of static bias stress and traps in the sensing process for the first time. Apart from electronic structure, defects at the molecular and microstructural levels govern the ability to form and sustain traps and subsequent backbone dopability. A polymer with a twisted backbone was observed to be capable of creating an energetically broad trap distribution while a polymer with a high degree of solid-state order shows a tendency to form an energetically narrow trap distribution and a fast passivation of traps on exposure to air. The stability and energetic distribution of traps on subjecting the polymers to bias stress was related to electronic structure and solid-state packing; and the ability of NO 2 and NH 3 to fill/create traps further was evaluated. At a bias stress condition of V G = V D = −80 V, the polymers retain their NO 2 sensitivity both post NO 2 -aided recovery and air-aided recovery. In order to verify the ability of NH 3 to create traps, traps were erased from the OFET sensors by charging with the aid of a positive gate voltage leading to an increase in the NH 3 response when compared to air controls. This work demonstrates that the charge-trap filling and generation response mechanism is predominant and can even be leveraged for higher responses to vapors. Backbone dopability appears to be a minor contributor to responses in this category of polymeric semiconductors with engineered defects. Finally, bias stress generally does not preclude this category of OFET vapor sensors from recovering their original sensitivities. 

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3. Bayesian Seismic Refraction Inversion for Critical Zone Science and Near‐Surface Applications

   https://doi.org/10.1029/2020GC009172  

   Huang, Mong‐Han; Hudson‐Rasmussen, Berit; Burdick, Scott; Lekic, Vedran; Nelson, Mariel D.; Fauria, Kristen E.; Schmerr, Nicholas (May 2021, Geochemistry, Geophysics, Geosystems)

   Abstract The critical zone (CZ) is the region of the Earth’s surface that extends from the bottom of the weathered bedrock to the tree canopy and is important because of its ability to store water and support ecosystems. A growing number of studies use active source shallow seismic refraction to explore and define the size and structure of the CZ across landscapes. However, measurement uncertainty and model resolution at depth are generally not evaluated, which makes the identification and interpretation of CZ features inconclusive. To reliably resolve seismic velocity with depth, we implement a Transdimensional Hierarchical Bayesian (THB) framework with reversible‐jump Markov Chain Monte Carlo to generate samples from the posterior distribution of velocity structures. We also perform 2D synthetic tests to explore how well THB traveltime inversion can resolve different subsurface velocity structures. We find that THB recovers both sharp changes in velocity as well as gradual velocity increases with depth. Furthermore, we explore the velocity structure in a series of ridge‐valley systems in northern California. The posterior velocity model shows an increasing thickness of low velocity material from channels to ridgetops along a transect parallel to bedding strike, implying a deeper weathering zone below ridgetops and hillslopes than below channels. The THB method enhances the ability to reliably image CZ structure, and the model uncertainty estimates it yields provides an objective way to interpret deep CZ structure. The method can be applied across other near‐surface studies, especially in the presence of significant surface topography. 

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4. The Gopher’s Gambit: Survival Advantages of Artifact-based Intention Perception [The Gopher’s Gambit: Survival Advantages of Artifact-based Intention Perception]

   https://doi.org/10.5220/0010207502050215  

   Hom, Cynthia; Maina-Kilaas, Amani; Ginta, Kevin; Lay, Cindy; Montañez, George (January 2021, Proceedings of the 13th International Conference on Agents and Artificial Intelligence)

   Being able to assess and calculate risks can positively impact an agent’s chances of survival. When other intelligent agents alter environments to create traps, the ability to detect such intended traps (and avoid them) could be life-saving. We investigate whether there are cases for which an agent’s ability to perceive intention through the assessment of environmental artifacts provides a measurable survival advantage. Our agents are virtual gophers assessing a series of room-like environments, which are potentially dangerous traps intended to harm them. Using statistical hypothesis tests based on configuration coherence, the gophers differentiate between designed traps and configurations that are randomly generated and most likely safe, allowing them access to the food contained within them. We find that gophers possessing the ability to perceive intention have significantly better survival outcomes than those without intention perception in most of the cases evaluated. 

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5. Rubbing-induced site-selective deposition of 2D material patterns on nanomembranes

   https://doi.org/10.1116/6.0003961  

   Chen, Mingze; An, Xiaoqiu; Ki, Seungjun; Liang, Xiaogan (December 2024, Journal of Vacuum Science & Technology B)

   2D-layered materials (e.g., graphene and transition metal dichalcogenides) have attracted huge attention due to their unique mechanical and electrical properties. Emerging research efforts, which seek to combine device characterization and high-resolution electron micrography analysis for 2D-layered device features, demand nano/microlithographic techniques capable of producing ordered 2D material patterns on ultrathin membranes with nanoscale thicknesses. However, such membranes are so fragile that most conventional lithographic techniques can be hardly performed on them to generate 2D material patterns. Our previous works have demonstrated that the rubbing-induced site-selective (RISS) deposition method can produce arbitrary 2D semiconductor (e.g., MoS2 and Bi2Se3) patterns on regular device substrates. This fabrication route prevents the vulnerable 2D-layered structures from the detrimental damage introduced by plasma etching and resist-based lithography processes. In this work, we explore the applicability of RISS for directly producing 2D material patterns on nanomembranes. Specifically, this work shows that a polymeric interfacing layer on the rubbing template features, which can effectively prevent stress concentration during the rubbing process, is crucial to successful implementation of RISS processes on nanomembranes. Furthermore, we carried out the mechanics simulation of the Von Mises stress and pressure distribution on the RISS-processed membrane to identify the optimal rubbing load, which can generate sufficient triboelectric charge for material deposition but no damage to the membrane. Using this approach, we have successfully demonstrated the deposition of Bi2Se3 patterns on 25 nm SiOx nanomembranes and high-resolution transmission electron micrography characterization of the crystallographic structures. 

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