Composite Electrodes With Immobilized Bacteria Bioanode and Photosynthetic Algae Biocathode for Bio-Batteries

A novel electrode was constructed and tested in a bio-battery. This configuration consisted of a composite electrode with immobilized bacteria (Escherichia coli K-12) in the anode and a composite electrode with immobilized Carbon Nanoparticles (CNP) and algae (Chlorella vulgaris/Scenedesmus sp.) suspended in the cathode. The composite electrode consisted of three parts: a 304L stainless steel mesh base, an electro-polymerized layer of pyrrole, and an electro-polymerized layer of methylene blue. The bacteria were immobilized on the anode electrode using a technique incorporating CNP and a Teflontm emulsion. The anode and cathode electrodes were tested separately in conjunction with chemical cathodes and anodes respectively. The composite electrode with immobilized bacteria was tested in a bioanode setup. The cathode chamber of the cell contained a potassium ferricyanide and buffer solution with a graphite electrode. Factors affecting electrode performance, such as Teflontm and carbon nanoparticle concentration, were investigated to find optimum values. The maximum power density generated by the composite electrode with immobilized bacteria and a chemical cathode was 378 mW/m2. This electrode configuration produced approximately 69% more power density and 53% more current density than composite electrodes with bacteria suspended in solution. Electrochemical Impedance Spectroscopy analysis determined that a significant portion of the bio-battery’s resistance to charge transfer occurred at the surface of the anode and this resistance was significantly lowered when using immobilized bacteria (51% lower than bio-batteries with suspended bacteria). Similarly, biocathodes containing composite electrodes coated with CNP were tested using two algae species, Chlorella vulgaris and Scenedesmus sp., suspended in solution. This electrode configuration was compared with composite electrode without CNP coating. The anode chamber contained potassium ferrocyanide solution with a graphite counter electrode. The composite electrode with CNP produced approximately 23% more current density than composite electrode without CNP. A complete bio-battery was designed using a composite electrode with immobilized bacteria anode and a CNP coated composite electrode with algae suspended in the cathode. EIS analysis showed that the resistance was higher in the biocathode than in the bioanode and a significant portion of the ohmic resistance was contributed by the membrane

Phase Noise Modeling of Opto-Mechanical Oscillators

We build upon and derive a precise far from carrier phase noise model for radiation pressure driven opto-mechanical oscillators and show that calculations based on our model accurately match published phase noise data for such oscillators. Furthermore, we derive insights based on the equations presented and calculate phase noise for an array of coupled disk resonators, showing that it is possible to achieve phase noise as low as -80 dBc/Hz at 1 kHz offset for a 54 MHz opto-mechanical oscillator

A Monolithic Radiation-Pressure Driven, Low Phase Noise Silicon Nitride Opto-Mechanical Oscillator

Cavity opto-mechanics enabled radiation pressure (RP) driven oscillators shown in the past offer an all optical Radio Frequency (RF) source without the need for external electrical feedback. However these oscillators require external tapered fiber or prism coupling and non-standard fabrication processes. In this work, we present a CMOS compatible fabrication process to design high optical quality factor opto-mechanical resonators in silicon nitride. The ring resonators designed in this process demonstrate low phase noise RP driven oscillations. Using integrated grating couplers and waveguide to couple light to the micro-resonator eliminates 1/f^3 and other higher order phase noise slopes at close-to-carrier frequencies present in previous demonstrations. We present an RP driven OMO operating at 41.97MHz with a signal power of -11dBm and phase noise of -85dBc/Hz at 1kHz offset with only 1/f^2 noise down to 10Hz offset from carrier

Human-machine cooperation for semantic feature listing

Semantic feature norms, lists of features that concepts do and do not possess, have played a central role in characterizing human conceptual knowledge, but require extensive human labor. Large language models (LLMs) offer a novel avenue for the automatic generation of such feature lists, but are prone to significant error. Here, we present a new method for combining a learned model of human lexical-semantics from limited data with LLM-generated data to efficiently generate high-quality feature norms.Comment: To be published in the ICLR TinyPaper trac

Learning interactions to boost human creativity with bandits and GPT-4

This paper considers how interactions with AI algorithms can boost human creative thought. We employ a psychological task that demonstrates limits on human creativity, namely semantic feature generation: given a concept name, respondents must list as many of its features as possible. Human participants typically produce only a fraction of the features they know before getting "stuck." In experiments with humans and with a language AI (GPT-4) we contrast behavior in the standard task versus a variant in which participants can ask for algorithmically-generated hints. Algorithm choice is administered by a multi-armed bandit whose reward indicates whether the hint helped generating more features. Humans and the AI show similar benefits from hints, and remarkably, bandits learning from AI responses prefer the same prompting strategy as those learning from human behavior. The results suggest that strategies for boosting human creativity via computer interactions can be learned by bandits run on groups of simulated participants

Conceptual structure coheres in human cognition but not in large language models

Neural network models of language have long been used as a tool for developing hypotheses about conceptual representation in the mind and brain. For many years, such use involved extracting vector-space representations of words and using distances among these to predict or understand human behavior in various semantic tasks. Contemporary large language models (LLMs), however, make it possible to interrogate the latent structure of conceptual representations using experimental methods nearly identical to those commonly used with human participants. The current work utilizes three common techniques borrowed from cognitive psychology to estimate and compare the structure of concepts in humans and a suite of LLMs. In humans, we show that conceptual structure is robust to differences in culture, language, and method of estimation. Structures estimated from LLM behavior, while individually fairly consistent with those estimated from human behavior, vary much more depending upon the particular task used to generate responses--across tasks, estimates of conceptual structure from the very same model cohere less with one another than do human structure estimates. These results highlight an important difference between contemporary LLMs and human cognition, with implications for understanding some fundamental limitations of contemporary machine language

Simulating Opinion Dynamics with Networks of LLM-based Agents

Accurately simulating human opinion dynamics is crucial for understanding a variety of societal phenomena, including polarization and the spread of misinformation. However, the agent-based models (ABMs) commonly used for such simulations often over-simplify human behavior. We propose a new approach to simulating opinion dynamics based on populations of Large Language Models (LLMs). Our findings reveal a strong inherent bias in LLM agents towards producing accurate information, leading simulated agents to consensus in line with scientific reality. This bias limits their utility for understanding resistance to consensus views on issues like climate change. After inducing confirmation bias through prompt engineering, however, we observed opinion fragmentation in line with existing agent-based modeling and opinion dynamics research. These insights highlight the promise and limitations of LLM agents in this domain and suggest a path forward: refining LLMs with real-world discourse to better simulate the evolution of human beliefs

The Wisdom of Partisan Crowds: Comparing Collective Intelligence in Humans and LLM-based Agents

Human groups are able to converge on more accurate beliefs through deliberation, even in the presence of polarization and partisan bias -- a phenomenon known as the "wisdom of partisan crowds." Generated agents powered by Large Language Models (LLMs) are increasingly used to simulate human collective behavior, yet few benchmarks exist for evaluating their dynamics against the behavior of human groups. In this paper, we examine the extent to which the wisdom of partisan crowds emerges in groups of LLM-based agents that are prompted to role-play as partisan personas (e.g., Democrat or Republican). We find that they not only display human-like partisan biases, but also converge to more accurate beliefs through deliberation as humans do. We then identify several factors that interfere with convergence, including the use of chain-of-thought prompt and lack of details in personas. Conversely, fine-tuning on human data appears to enhance convergence. These findings show the potential and limitations of LLM-based agents as a model of human collective intelligence