Knowledge Structure and Frontier Evolution of Research on Nickel Deposits

Abstract

Nickel (Ni) resources are critical for the development of modern industry. This study investigates the knowledge structure and frontier evolution of Ni deposit research by constructing a domain-specific knowledge graph using bibliometric analysis and semantic extraction from 7090 publications (1966–2024) sourced from the Web of Science Core Collection. The results show that Ni research has three distinct phases: slow growth (1966–1990), early growth (1991–2010), and rapid expansion (2011–present). The collaborative network of institutions in which articles are published forms three regional clusters centered on China, Russia, and Australia. Keyword burst analysis identifies emerging frontiers, including sulfur isotopes, pyrite geochemistry, and LA-ICP-MS applications. Temporal keyword analysis identifies “platinum-group minerals”, “ore-forming fluids”, “isotopic analysis”, and “Eastern Tianshan” interactions as five pivotal research frontiers in nickel deposit studies. The knowledge graph framework demonstrates significant potential in mitigating data fragmentation, enhancing interdisciplinary data accessibility, and guiding future exploration strategies. This study shows the important role of knowledge maps in optimizing the study of nickel deposits.

1. Introduction

The global distribution of nickel (Ni) resources and production patterns have changed significantly over the past decade, highlighting the critical role of nickel in the modern industrial system [1,2,3]. Global nickel resources are estimated to be 350 million tonnes. Among them, 54% are in laterite, 35% in magmatic sulfide deposits, and the rest are distributed in hydrothermal sulfide deposits, sedimentary polymetallic deposits, and submarine manganese crusts (all data from U.S. Geological Survey [4]). The specific characteristics of nickel mineral production types lead to huge regional differences in mining methods and resource content. Production data from 2014 to 2023 show that Indonesia has experienced exponential growth in both production and reserves, making it the dominant producer (Supplementary Materials Table S1). This contrasts with the stable production levels in Australia, Russia, and Canada, while the contribution of the United States has remained at the lowest level (Figure 1). With the recent boom of the new-energy industry in recent years, the key role of nickel in lithium-ion batteries has further emphasized its importance [5,6]. The rapid expansion of downstream industries and the insufficient supply of upstream resources indicate the urgent need to upgrade the theory of nickel ore to ensure the long-term viability of the industry.

In recent years, the technological revolution integrating big data, cloud computing, and artificial intelligence (AI) has been reshaping the approach to scientific research [7,8]. Second-generation AI technologies that utilize deep learning architectures show superior performance in pattern recognition and complex data processing tasks [9,10,11]. The emergence of big data has profoundly changed the way humans live and produce, and has also led to the transformation of the scientific research paradigm to the fourth paradigm driven by big data [12]. In the field of Earth sciences, big data also shows great potential, providing researchers with unique ideas to gain a deeper understanding of the human–Earth system from different perspectives [13]. In the past few years, a wealth of Earth science data, including observations from various types of Earth information obtained through various research methods, have been carefully described by Earth scientists in authoritative journals and published in open-source formats, making a significant contribution to Earth science research [14,15]. However, managing these data resources across different platforms, which involve different data models and storage mechanisms, leads to decentralized storage and semantic flaws. This presents challenges for users attempting to obtain comprehensive data. Especially for users from non-Earth science fields, they face greater barriers in analyzing and discovering the data they need due to a lack of interdisciplinary expertise [7,15,16,17]. To solve these problems, the knowledge graph, as an effective technical means, has emerged. By extracting semantic knowledge from the relevant literature, it promotes the labeling and linking of different resources and builds a comprehensive knowledge network. This alleviates the fragmentation of the individual literature or data resources on the internet and increases the efficiency of data discovery and analysis [18]. In the context of the explosive growth of Earth science data and the gradual shift in Earth science research to the fourth paradigm driven by big data, application of the knowledge graph is particularly important [7,19,20]. In fact, the exploitation of mineral resources and related scientific research output is mutually promoting [21]. However, the explosive growth in research can also lead to confusion regarding the knowledge structure, so how to sort out these knowledge points becomes extremely important. Middle-range theorizing is a good way to solve this problem [22]. As a professional bibliometric analysis tool, CiteSpace has many advantages in constructing a knowledge graph and summarizing knowledge [23]. It can transform fragmented literature data into a systematic knowledge graph, which not only helps researchers efficiently summarize domain knowledge but also aids in identifying research gaps and predicting development trends [24]. Its advantages are particularly evident in interdisciplinary research, long-term trend analysis, and complex knowledge network construction. VOSviewer (version 1.6.20) is also mainstream software in the field of bibliometrics and scientific knowledge graph analysis [25,26]. The algorithm of VOSviewer is based on multi-dimensional scaling analysis (MDS) of the literature similarity matrix, optimizes the node layout by minimizing the Stress Function, and is suitable for processing large-scale networks with millions of publications [27,28,29,30]. Community detection is conducted using the Louvain algorithm, and the results are distinguished by color for different research topics [27,28,29,30]. The choice between VOSviewer and CiteSpace should be made in consideration of research objectives, data characteristics, and user skills [25,26]. When aiming for efficient visualization and large-scale data processing, VOSviewer is a preferable option. In cases where in-depth exploration of the temporal evolution pattern is required, CiteSpace offers more advantages. In practical research, the combined application of these two tools can fully leverage their respective strengths, thus providing a more comprehensive perspective for interdisciplinary research.

In this study, CiteSpace software and VOSviewer were employed to conduct visual knowledge mapping for analyzing emerging trends and intellectual structures in nickel deposit research. By systematically extracting structured semantic relations from the geological literature, this method establishes a comprehensive knowledge graph of nickel deposit research, enhancing the ability to identify current research hotspots and track emerging trends in the field. Thus, this study leverages such analytical advantages to effectively facilitate the theoretical advancements in nickel resource prospecting, providing a scientific basis for addressing key challenges in this domain.

2. Data and Analytical Methods

2.1. Data Sources

The data utilized in this study were retrieved from the Web of Science Core Collection (WoSCC), a premier multidisciplinary database indexed across natural sciences, social sciences, arts, and humanities disciplines. To ensure representativeness and reproducibility, we performed a systematic literature search through the Web of Science Core Collection on 16 July 2023. The search topic was set to “Ni deposit”, and the research direction was set to “geology” OR “geochemistry” OR “geophysics” OR “mineralogy” OR “mining and mineral processing”. In addition, patents, conference abstracts and non-academic articles were excluded. A total of 7090 literature records were collected to form the basic dataset. These records included author(s), title, source, times cited, accession number, and abstract. All records were saved as text files. A complete text files is provided in Supplementary Materials S2.

2.2. Analytical Methods

In this study, CiteSpace (version 6.2.R4) software was used for analysis. CiteSpace can precisely identify high-frequency topics, core nodes, and emerging research directions within a field by leveraging indicators such as keyword frequency, centrality, and burst detection. It supports co-citation analysis and coupling analysis, which can uncover latent relationships among documents and pinpoint classical theoretical foundations (highly co-cited documents) and emerging research clusters (tightly coupled literature groups). The features of node density and connection strength in the knowledge graph can assist in evaluating the maturity of research in the field. For instance, a high-density network may indicate a well-established research system, whereas a sparse network might suggest the existence of research gaps. Moreover, CiteSpace can identify the literature that has a high citation count but low centrality, enabling the discovery of potentially significant yet overlooked studies. This process provides valuable insights for research innovation. The time range is from 1966 to 2024, divided into 10-year intervals (time slices). To optimize network clarity, the top 25 most frequent nodes of each slice are retained, and cosine similarity (Formula (1)) is applied to quantify the link strength between nodes, thereby minimizing redundancy. We selected institutions, keywords, citation journals, and countries as the clustering basis, and automatically clustered them to generate knowledge maps. The cosine similarity measure is defined as

$$\cos ({\mathit{C}}_{\mathit{ij}},{\mathit{S}}_{\mathit{i}}\ast {\mathit{S}}_{\mathit{j}})\mathit{x},\mathit{y}={\displaystyle \frac{\mathit{X}\mathit{Y}}{\mathit{X}\mathit{Y}}}={\displaystyle \frac{{\mathit{C}}_{\mathit{i}\mathit{j}}}{\sqrt{{\mathit{S}}_{\mathit{i}}\ast {\mathit{S}}_{\mathit{j}}}}}$$

(1)

where C_ij denotes co-occurrence frequency between nodes i and j, and S_i and S_j represent their individual frequencies. Normalized values range from 0 (no association) to 1 (perfect association). Methodological details are further elaborated in Chen et al. (2004) [23] and Zuo et al. (2021) [24].

VOSviewer (version 1.6.20) is a widely utilized tool in bibliometrics that enables the creation of detailed bibliographic networks, encompassing authors, institutions, and collaborating countries or regions [25,26,27,28,29,30]. The software facilitates the analysis of collaboration networks through various methods, such as keyword co-occurrence, bibliographic coupling, co-citation, and co-authorship. In this study, keyword analysis was employed to identify the relevance of research domains in the field, including countries, institutions, and cited journals. For detailed analytical procedures, refer to References [25,26,27,28,29,30].

3. Results and Discussion

3.1. Annual Publishing Trends

The number of articles is a key indicator of the popularity of the research, which can reflect the development and future prospects of the topic [20]. Figure 2 shows the number of annual articles on nickel deposit research from 1966 to 2024. The development of this research can be divided into three stages: (1) Slow early growth stage (1966–1990): in this initial stage, the research output remained low, averaging less than 10 papers per year. (2) Accelerated growth phase (1991–2010): Publication numbers surged to 1914 papers (annual average: ~100), driven by advancements in geochemical analytical techniques and the discovery of major Ni deposits in Canada and Australia. This phase also saw diversification into new subfields, including platinum-group element (PGE) geochemistry and magmatic sulfide deposits. (3) Exponential growth stage (2011–2024): With the innovation of high-precision geochemical analysis methods, the research output rose sharply to 5057 articles (average annual number > 300). This period marked the transition from the study of mineral deposit investigation to the study of mineral deposit geochemistry.

3.2. Quantitative Analysis of WoS Knowledge Mapping Results

3.2.1. Institutions Cooperation Network

A pathfinder-pruned network analysis (10-year time slices) identified 631 nodes (institutions) and 952 collaboration links, yielding a network density of 0.0048 (Figure 3a, Supplementary Materials Table S2). Node size corresponds to publication frequency, while link thickness denotes collaboration intensity. Three dominant clusters emerged: Cluster 1: Chinese institutions led by the Chinese Academy of Sciences (CAS). Cluster 2: European and North American institutions centered on CNRS (France), emphasizing magmatic Ni-Cu-PGE systems. Cluster 3: Russian institutions anchored by the Russian Academy of Sciences (RAS), focusing on Norilsk-type deposits. In addition, Australian institutions—CSIRO, University of Tasmania, and University of Western Australia—occupied central network positions, bridging regional clusters and facilitating knowledge exchange on lateritic Ni deposits.

The results of the Institutions cooperation network using VOSviewer are presented in Figure 3b. Based on color, the classification yields three categories: The first category predominantly comprises Chinese research institutions, including the Chinese Academy of Sciences (CAS), China University of Geosciences, China Geological Survey, Nanjing University, and the University of Chinese Academy of Sciences, among others. The second category primarily consists of research institutions from European and American countries, such as the University of Western Australia, the Centre National de la Recherche Scientifique (CNRS), and the Commonwealth Scientific and Industrial Research Organisation (CSIRO), among others. The third category is mainly composed of the Russian Academy of Sciences.

3.2.2. Countries/Regions Cooperation Network

The global collaboration network comprises 141 nodes and 1066 links, with a density of 0.108. The top five countries ranked in order are China, Canada, Australia, Russia, and the USA. China was the largest contributor, accounting for 26% of publications, while Australia emerged as the central hub for international collaboration (Figure 4a, Supplementary Materials Table S3). This centrality likely stems from Australia’s role as a major nickel producer and its emphasis on interdisciplinary research programs. The network exhibits a scale-free structure, with 85% of countries connected through Australia, China, or the United States, highlighting the globalization of nickel deposit research.

The results of the countries/regions cooperation network using VOSviewer software are presented in the Figure 4b. Based on the color-based classification, it is clearly evident that there are five categories. The first category includes China and Japan. The second category consists of Canada, the USA, Italy, Turkey, Spain, India, and Iran. The third category comprises Russia, Germany, Austria, Finland, and Poland. The fourth category contains Australia, the UK, South Africa, and Wales. The fifth category includes France and Brazil. Evidently, China, Canada, Australia, and the United States have a very robust cooperative network.

3.2.3. Journal Citation Relationship from References

Analysis of journal citation patterns (2073 nodes, 2593 links; density = 0.0012) identified Geochimica et Cosmochimica Acta as the most cited journal (Figure 5a, Supplementary Materials Table S4), reflecting its dominance in geochemical studies of Ni systems. High-impact journals included Chemical Geology (Ni isotope applications), Economic Geology (ore genesis models), and Mineralium Deposita (mineralization processes). The prominence of interdisciplinary journals (e.g., Nature, Science) signals growing integration of Ni deposit research with planetary science and environmental studies.

The results of the analysis of journal citation relationships in the references using VOSviewer are presented in Figure 5b. It has been divided into four categories. The first category comprises journals related to geochemistry, such as Geochimica et Cosmochimica Acta, Chemical Geology, Journal of Geochemical Exploration, and GSA Bulletin. The second category consists of journals focused on ore deposits, including Economic Geology, Mineralium Deposita, Journal of Petrology, Contributions to Mineralogy and Petrology, The Canadian Mineralogist, and American Mineralogist. The third category includes journals related to rock structures, such as Ore Geology Reviews, Lithos, Precambrian Research, Journal of Asian Earth Sciences, and Tectonophysics. The fourth category encompasses comprehensive journals, including Nature, Science, Geology, and Earth and Planetary Science Letters.

3.3. Burstiness Analysis of Keywords

Keyword burst analysis reveals evolving research priorities [24]. In Figure 6, the start and end of the burst period are denoted by red line segments. There are 20 keywords with the highest burst intensity (Figure 6). Phase 1 (1968–1990): Focused on foundational petrogenetic terms (e.g., “Palladium”, “ultramafic rocks”, “stratiform deposits”). Phase 2 (1991–2010): Shifted toward regional Ni systems (e.g., “Sudbury Basin”, “Western Australia”) and PGE-sulfide geochemistry. Phase 3 (2011–2023): Prioritized microanalytical techniques (e.g., “sulfur isotopes”, “LA-ICP-MS”) and mineral-specific studies (e.g., “pyrite”), driven by advances in spatially resolved geochemistry.

3.4. The Research Frontier for Nickel Deposit

CiteSpace provides automatic clustering functionality based on the spectral clustering algorithm. Three algorithms are introduced to extract cluster theme terms from clustered citation documents. The default automatic labeling terms are derived from the LLR algorithm [24]. To enhance the readability of the knowledge graph, pruning is necessary. Pruning removes insignificant nodes and connections, thereby highlighting significant nodes and connections within the network. For the pruning algorithm, we employ the Minimum Spanning Tree (MST) algorithm with the “pruning merged network” strategy. The evolving understanding of nickel deposit systems demands a paradigm shift in research methodologies. This study synthesizes recent breakthroughs to outline five critical research axes: (1) platinum-group mineral; (2) ore-forming fluids; (3) isotopic analysis; (4) Eastern Tianshan; and (5) organic matter. Each dimension reveals fundamental scientific challenges requiring interdisciplinary collaboration (Figure 7).

3.4.1. Platinum-Group Minerals

Platinum-group minerals (PGMs) are important metallic minerals in ultramafic rocks [31,32]. Their metallogenic processes are considered to be significantly associated with nickel ore mineralization [3,33,34,35,36,37,38,39,40,41]. PGMs serve as critical tracers for elucidating the evolution of sulfide melt and crustal contamination in magmatic nickel–copper deposits [42]. Recent studies have emphasized that platinum-group metals (e.g., sperrylite, iridium) form through sulfide liquid segregation, fractional crystallization, and interaction with late-stage fluids [43,44,45]. For example, Mansur et al. (2021) [45] demonstrated the differential compatibility of PGE during sulfide crystallization: Pd and Rh are preferentially partitioned into nickel-rich pyrite, while IPGE (Os, Ir, and Ru) are concentrated in arsenopyrite. This partitioning behavior reflects the physicochemical conditions of sulfide melt evolution, such as temperature and sulfur fugacity. In the Fazenda Mirabela intrusion (Brazil), zonal patterns of PGMs indicate multiple sulfide melt injections and interactions with arsenic-rich hydrothermal fluids, highlighting the dynamic nature of the sulfide melt–fluid system [44]. Crustal assimilation plays a key role in PGM nucleation. Barnes et al. (2016) showed that the introduction of semi-metals (As, Sb, and Bi) via crustal contamination reduces the activation energy for PGM formation [43]. These elements alter the chemistry of sulfide melts and facilitate the precipitation of discrete PGMs from base metal sulfides (BMS). For instance, in PGE-dominated deposits, TABS (Te, As, Bi, and Sb) in nickel pyrite exhibit a negative correlation with PGE concentrations, suggesting that PGM precipitation removes these semi-metals from the BMS lattice [45]. These findings underscore the dual control of PGM distribution by magmatism and crustal inputs. However, challenges persist in quantifying the stability of PGMs under varying sulfur fugacities and their role in enhancing PGM concentrations during ore formation.

3.4.2. Ore-Forming Fluids

Ore-forming fluids play a central role in the formation and evolution of nickel deposits, serving as the primary medium for metal migration and sulfide precipitation [46,47]. In magmatic Ni-Cu systems, sulfide saturation—critical for ore formation—is triggered by crustal contamination (e.g., assimilation of sulfur-rich metamorphic sediments), magma mixing, or volatile exsolution [3,33,34,35,36,37,38,39,40,41]. These processes highlight the interaction between magmatic differentiation and hydrodynamics during mineralization. Hydrothermal systems exhibit unique fluid behaviors. In the Cerro Pabellón geothermal system, nickel enrichment in pyrite results from the instability of Ni-Cl complexes during fluid boiling and cooling [48]. Similarly, in komatiite-hosted nickel systems (e.g., Kambalda, Western Australia), thermal erosion of sulfide deposits by komatiite lava generates sulfide-saturated melts, and structural pipes focus fluid flow to form massive ores [49]. Advances in analytical techniques, such as clumped isotope measurement and laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS) fluid inclusion analysis, now enable precise reconstruction of fluid temperature, salinity, and metal budgets, resolving previously ambiguous issues in fluid evolution [50]. However, challenges persist in understanding the transition from magmatic to hydrothermal states. The integration of geophysical (magnetotelluric, seismic) and geochemical data facilitates mapping of subsurface fluid pathways in key ore-forming provinces like the Eastern Tianshan Mountains, where deep faults control the emplacement of buried nickel-bearing intrusions [51]. Coupling radiogenic isotopes with stable metal isotopes can refine fluid source models, while machine learning predicts fluid-related mineralization patterns by analyzing trace element signatures in sulfides.

3.4.3. Isotopic Analysis

The isotopic characteristics of different deposits reflect diverse sulfur sources (evaporite, sedimentary sulfide, and deep magma) and metallogenic environments. These characteristics provide important geochemical constraints for establishing genetic models and formulating prospecting strategies [49,52,53,54,55]. Sulfur isotopes are the key tracers for explaining the source of sulfur, the interaction between magma and surrounding rocks, and the sulfur saturation mechanism during nickel mineralization. The heavy sulfur isotopic composition (δ³⁴S = +8‰~+12‰) [56] of the Norilsk deposit indicates that the decomposition of evaporative karst (e.g., gypsum/anhydrite) is the dominant contributor of crustal sulfur. These isotopic signatures suggest that the interaction between magma and sulfur-rich surrounding rocks triggers sulfur saturation through crustal assimilation, ultimately leading to the separation of sulfide melt. In contrast, the Duluth Complex has a wider range of sulfur isotopes (δ³⁴S = +0.2‰ to +16‰), which is consistent with the isotopic signature of the underlying Virginia Formation sedimentary rocks [52]. This correlation suggests that the source of sulfur is the sulfide-rich horizon in the graphitic metamorphosed sediments and reservoirs [52]. The presence of negative to near-zero Δ³³S values in sulfide ores further emphasizes crustal assimilation [57], especially the transformation of mantle derived melts [58] involving sulfur-rich Archean sedimentary basins. These isotopic variations highlight the heterogeneity of crustal contributions, with specific tectonic environments (such as sedimentary basin margins) enabling the selective assimilation of sulfur reservoirs [59]. The systematic correspondence between the sulfur isotopic composition of the deposit and its host lithology, such as the Norilsk–evaporite and Duluth–Virginia Formation assemblages, strongly supports the view that the addition of crustal sulfur is the main driver of magma sulfur saturation. This interaction not only provides sulfur but also alters the oxidation state of the magma and the solubility of sulfur through contamination, thus facilitating the separation of sulfide melt and the subsequent enrichment of Ni, Cu, and associated metals [52,60]. The degree of crustal contamination is generally not equivalent to the degree of crustal sulfur contamination [61]. For example, in the Xialihamu Ni-Co deposit, the two-endmember Hf isotope mixing model indicates that the parental magma experienced 15% crustal contamination, which is significantly lower than the 41.8% crustal sulfur contamination calculated by the corresponding S isotope two-endmember mixing model [61,62]. This discrepancy highlights that crustal contamination and crustal sulfur contamination represent distinct processes. Assuming that all crustal contamination promotes sulfide saturation in mantle-derived magmas is overly simplistic. Previous studies have shown that certain crustal components promote sulfide saturation, whereas some crustal materials inhibit it [57,63]. Therefore, using isotopic methods to distinguish these two types of crustal contamination represents an important scientific frontier. Recent progress in metal stable isotope systems has rendered Cu and Ni isotopes an innovative means for the research on copper–nickel deposits [64]. However, the two isotope systems exhibit similarities in geochemical behavior [65,66]. For instance, neither displays significant fractionation during the crystallization of silicate minerals [65,66]. However, they respond quite differently to the fractional crystallization of sulfides [65,66]. Sulfide segregation induces measurable fractionation in both Cu (δ⁶⁵Cu) and Ni (δ⁶⁰Ni) isotopes, with Ni isotopes typically showing negative values in sulfide ores, whereas Cu isotopes exhibit bidirectional fractionation [67,68]. This disparity might mirror different redox sensitivities, as changes in oxygen fugacity exert a stronger control over Cu isotope fractionation. Notably, the processes associated with subduction and partial melting give rise to distinct Cu isotope fractionation but seem to overlook the influence on Ni isotopes [65,69]. Therefore, the complementarity between the Cu and Ni isotope systems offers novel perspectives on the geodynamic background and metal enrichment mechanisms, and it also provides a dual-isotope framework for constraining magmatic evolution and mineralization processes.

3.4.4. Eastern Tianshan

The East Tianshan Orogenic Belt is a key nickel–copper deposit region in northwestern China. It hosts world-class deposits such as Huangshandong and Tulargen, providing critical insights into post-collision magmatic processes and sulfide mineralization [70,71,72,73,74]. Genetically linked to Late Carboniferous–Early Permian (290–270 Ma) ultramafic–mafic intrusions, these deposits formed during the post-collision extensional stage following the closure of the Paleo-Asian Ocean [3,75,76,77,78,79]. Mantle-derived magmas, likely sourced from metasomatized lithospheric mantle, attain sulfide saturation through crustal sulfur assimilation. However, the low sulfur content in local crustal rocks often results in disseminated rather than massive sulfide ores [80,81]. This highlights the crucial role of crustal contamination in triggering sulfide liquid immiscibility, even in regions with limited sulfur availability [3]. Geophysical surveys integrating magnetotelluric and seismic reflection data have identified a concealed intrusion along deep faults, underscoring the exploration potential of the East Tianshan Belt [51]. These structures may serve as pathways for magma ascent and localized sulfide accumulation. A remaining challenge is the scarcity of crustal sulfur, which raises questions about alternative sulfide saturation mechanisms. Mungall and Brenan (2014) [42] proposed that magma mixing can enhance sulfur solubility in melts without extensive crustal assimilation—a hypothesis requiring further experimental validation but capable of explaining economic ore formation in sulfur-deficient settings. Structural controls, particularly within the Agnew-Wiluna greenstone belt analogs, facilitated the concentration of sulfide melts into high-grade “chonolith” ore bodies via steeply inclined channels that focused metal-rich fluids [82]. Understanding the interplay between tectonic evolution and metallogenic stages in East Tianshan remains a key research objective. The Late Paleozoic transition from compression to extension likely promoted nickel–copper magma ascent, while subsequent uplift and erosion influenced ore preservation. In summary, East Tianshan represents a unique metallogenic system where Ni-Cu sulfide formation is driven by post-collision tectonics, mantle magmatism, and crustal interactions. Future investigations should integrate geodynamic modeling with high-precision chronology to resolve the spatiotemporal linkage between tectonic events and mineralization. Additionally, advanced exploration techniques, such as hyperspectral imaging and machine learning-driven geochemical analysis, could enhance the detection of subtle alteration halos or weathered sulfide remnants in this arid region.

3.4.5. Organic Matter

Organic enrichment in nickel ore has long been a research hotspot [83,84,85,86]. In the mineralization process of laterite-type nickel deposits, organic acids generated by organic matter decomposition act on the weathering of ultrabasic rocks, leading to the release of Ni²⁺ [85,87]. Under neutral-to-alkaline conditions, these ions are adsorbed by Fe-Mn hydroxides or bound to clay minerals [87]. The nickel content in pyrite deposited within nickel ores can reach 1000 ppm [88]. The formation of nickel-bearing pyrite in black shales is directly associated with organically mediated complexation, indicating that organic matter plays a critical role in nickel stability in sulfur-rich environments [89]. Notably, during diagenesis, organic matter facilitates sulfide formation by creating a reducing environment that sequesters nickel [88]. Consequently, it has been proposed that organic matter immobilizes nickel during diagenesis through organometal complexation (e.g., thiol organic phases) [88,89]. However, the interaction between organic-rich fluids and magmatic–hydrothermal systems remains poorly understood. How do organic ligands such as mercaptans and carboxylic acids influence nickel migration at elevated temperatures? Do organic substances participate in the formation or degradation of high-temperature sulfide melts?

Future research hotspots in nickel ore geology will focus on theoretical innovation and technological breakthroughs in deep-seated mineralization, efficient exploitation of laterite nickel deposits, sustainable utilization of deep-sea resources, and intelligent technology-driven green exploration. In the domain of magmatic sulfide deposits, studies breaking through traditional cognitions will combine high-temperature/high-pressure experiments with isotope tracing technology to analyze the dynamic mechanisms of sulfide immiscibility and segregation, thereby promoting the accurate targeting of deep-hidden ore bodies. The research emphasis for laterite-type nickel deposits has shifted to optimizing processing technologies for low-grade ores. The exploration and development of deep-sea polymetallic nodules face the challenge of balancing ecological protection and resource demand. The integration of geological big data and artificial intelligence is expected to significantly enhance exploration efficiency.

4. Conclusions

This study synthesizes global research trends and knowledge evolution in Ni deposit systems through a comprehensive bibliometric and knowledge graph approach. Evidently, in the co-occurrence network analysis of countries, institutions, and journals, the output images of VOSviewer are more aesthetically pleasing and straightforward compared to those of CiteSpace. Key findings highlight the exponential growth of Ni-related research since 2011. Institutional collaboration networks emphasize China’s dominance, alongside Australia’s central role in global partnerships. The identification of sulfur isotopes and LA-ICP-MS as burst keywords reflects methodological shifts toward high-resolution geochemical analyses. Critical research frontiers, such as organic matter-mediated nickel stabilization and platinum-group mineral dynamics, underscore the need for interdisciplinary integration to address unresolved mechanisms, including high-temperature ligand interactions and crustal assimilation processes. The knowledge graph framework effectively addresses data fragmentation challenges, enabling efficient resource integration and discovery. In fact, there are still limitations in this study, including how to evaluate multiple bibliometric databases and the non-English literature. Future research should prioritize experimental validation of magmatic–hydrothermal transitions, machine learning-driven exploration targeting, and geodynamic modeling to unravel complex Ni mineralization pathways.